human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "energy efficiency of downlink transmission strategies for cloud radio access networks"
  Abstract: This paper studies the energy efficiency of the cloud radio access network (C-RAN), specifically focusing on two fundamental and different downlink transmission strategies, namely the data-sharing strategy and the compression strategy. In the data-sharing strategy, the backhaul links connecting the central processor (CP) and the base-stations (BSs) are used to carry user messages -- each user's messages are sent to multiple BSs; the BSs locally form the beamforming vectors then cooperatively transmit the messages to the user. In the compression strategy, the user messages are precoded centrally at the CP, which forwards a compressed version of the analog beamformed signals to the BSs for cooperative transmission. This paper compares the energy efficiencies of the two strategies by formulating an optimization problem of minimizing the total network power consumption subject to user target rate constraints, where the total network power includes the BS transmission power, BS activation power, and load-dependent backhaul power. To tackle the discrete and nonconvex nature of the optimization problems, we utilize the techniques of reweighted $\ell_1$ minimization and successive convex approximation to devise provably convergent algorithms. Our main finding is that both the optimized data-sharing and compression strategies in C-RAN achieve much higher energy efficiency as compared to the non-optimized coordinated multi-point transmission, but their comparative effectiveness in energy saving depends on the user target rate. At low user target rate, data-sharing consumes less total power than compression, however, as the user target rate increases, the backhaul power consumption for data-sharing increases significantly leading to better energy efficiency of compression at the high user rate regime.

Connected through hyperedge HE-1 (induced by source paper "sparse optimization for green edge ai inference"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "exploiting computation replication for mobile edge computing a fundamental computation communication tradeoff study"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "mobile edge intelligence and computing for the internet of vehicles"
    - "energy efficient processing and robust wireless cooperative transmission for edge inference"
    - "the roadmap to 6g ai empowered wireless networks"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "data shuffling in wireless distributed computing via low rank optimization"

Connected through hyperedge HE-2 (induced by source paper "hybrid precoder design for cache enabled millimeter wave radio access networks"):
  with members:
    - "hybrid beamforming based on implicit channel state information for millimeter wave links"
    - "two level transmission scheme for cache enabled fog radio access networks"
    - "optimized base station cache allocation for cloud radio access network with multicast backhaul"
    - "spatial and frequency wideband effects in millimeter wave massive mimo systems"
    - "joint precoding and rrh selection for user centric green mimo c ran"
    - "energy efficient transceiver design for hybrid sub array architecture mimo systems"
    - "joint design of digital and analog processing for downlink c ran with large scale antenna arrays"
    - "codebook based hybrid precoding for millimeter wave multiuser systems"

Connected through hyperedge HE-3 (induced by source paper "energy efficient resource allocation in uav enabled mobile edge computing networks"):
  with members:
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "liquid state machine learning for resource and cache management in lte u unmanned aerial vehicle uav networks"
    - "uav aided offloading for cellular hotspot"
    - "joint altitude beamwidth location and bandwidth optimization for uav enabled communications"
    - "stochastic optimization and control framework for 5g network slicing with effective isolation"
    - "joint energy minimization and resource allocation in c ran with mobile cloud"
    - "placement optimization of uav mounted mobile base stations"

Connected through hyperedge HE-4 (induced by source paper "energy efficient processing and robust wireless cooperative transmission for edge inference"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "reliable federated learning for mobile networks"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "two timescale hybrid compression and forward for massive mimo aided c ran"
    - "wireless network intelligence at the edge"
    - "nonconvex and nonsmooth sparse optimization via adaptively iterative reweighted methods"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "limited feedback in single and multi user mimo systems with finite bit adcs"

Connected through hyperedge HE-5 (induced by source paper "resource allocation using gradient boosting aided deep q network for iot in c rans"):
  with members:
    - "bit level optimized neural network for multi antenna channel quantization"
    - "learning to optimize training deep neural networks for interference management"
    - "an introduction to deep reinforcement learning"
    - "mimo channel information feedback using deep recurrent network"
    - "deep forest"
    - "model driven deep learning for physical layer communications"
    - "deep forest towards an alternative to deep neural networks"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-6 (induced by source paper "power efficient resource allocation in c rans with sinr constraints and deadlines"):
  with members:
    - "joint energy minimization and resource allocation in c ran with mobile cloud"
    - "base station on off switching in 5g wireless networks approaches and challenges"
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "joint optimization of cloud and edge processing for fog radio access networks"
    - "inter tier interference suppression in heterogeneous cloud radio access networks"
    - "energy efficient resource assignment and power allocation in heterogeneous cloud radio access networks"
    - "large scale convex optimization for ultra dense cloud ran"
    - "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies"

Connected through hyperedge HE-7 (induced by source paper "joint sparse beamforming and power control for large scale das with network assistant full duplex"):
  with members:
    - "performance of network assisted full duplex for cell free massive mimo"
    - "fractional programming for communication systems part ii uplink scheduling via matching"
    - "cross layer design for downlink multihop cloud radio access networks with network coding"
    - "analysis of massive mimo enabled downlink wireless backhauling for full duplex small cells"
    - "d2d enhanced heterogeneous cellular networks with dynamic tdd"
    - "compflex comp for in band wireless full duplex"
    - "on the spectral efficiency of full duplex small cell wireless systems"
    - "sparse beamforming and user centric clustering for downlink cloud radio access network"

Connected through hyperedge HE-8 (induced by source paper "robust beamforming with pilot reuse scheduling in a heterogeneous cloud radio access network"):
  with members:
    - "joint precoding and rrh selection for user centric green mimo c ran"
    - "joint user selection and energy minimization for ultra dense multi channel c ran with incomplete csi"
    - "energy efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks"
    - "robust pilot decontamination based on joint angle and power domain discrimination"
    - "energy efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks"
    - "contract based interference coordination in heterogeneous cloud radio access networks"
    - "coordinated shared spectrum precoding with distributed csit"
    - "energy efficient resource assignment and power allocation in heterogeneous cloud radio access networks"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.IT(Information Theory)