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: "group sparse beamforming for green cloud ran"
  Abstract: A cloud radio access network (Cloud-RAN) is a network architecture that holds the promise of meeting the explosive growth of mobile data traffic. In this architecture, all the baseband signal processing is shifted to a single baseband unit (BBU) pool, which enables efficient resource allocation and interference management. Meanwhile, conventional powerful base stations can be replaced by low-cost low-power remote radio heads (RRHs), producing a green and low-cost infrastructure. However, as all the RRHs need to be connected to the BBU pool through optical transport links, the transport network power consumption becomes significant. In this paper, we propose a new framework to design a green Cloud-RAN, which is formulated as a joint RRH selection and power minimization beamforming problem. To efficiently solve this problem, we first propose a greedy selection algorithm, which is shown to provide near- optimal performance. To further reduce the complexity, a novel group sparse beamforming method is proposed by inducing the group-sparsity of beamformers using the weighted $\ell_1/\ell_2$-norm minimization, where the group sparsity pattern indicates those RRHs that can be switched off. Simulation results will show that the proposed algorithms significantly reduce the network power consumption and demonstrate the importance of considering the transport link power consumption.

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 "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-3 (induced by source paper "signal processing for high throughput satellites challenges in new interference limited scenarios"):
  with members:
    - "clustering strategies for multicast precoding in multi beam satellite systems"
    - "generalized multicast multibeam precoding for satellite communications"
    - "robust transmission in downlink multiuser miso systems a rate splitting approach"
    - "admm based fast algorithm for multi group multicast beamforming in large scale wireless systems"
    - "resource management in non orthogonal multiple access networks for 5g and beyond"
    - "massive mimo ten myths and one critical question"
    - "hybrid beamforming for massive mimo a survey"
    - "dvb s2x enabled precoding for high throughput satellite systems"

Connected through hyperedge HE-4 (induced by source paper "interference mitigation via rate splitting and common message decoding in cloud radio access networks"):
  with members:
    - "energy efficiency of rate splitting multiple access and performance benefits over sdma and noma"
    - "rate splitting for multi antenna non orthogonal unicast and multicast transmission"
    - "cross layer design for downlink multihop cloud radio access networks with network coding"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "optimality of treating interference as noise a combinatorial perspective"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"
    - "cloud radio access networks with coded caching"

Connected through hyperedge HE-5 (induced by source paper "provisioning energy efficiency and qos for multi carrier comp with limited feedback"):
  with members:
    - "performance analysis and optimization for interference alignment over mimo interference channels with limited feedback"
    - "what will 5g be"
    - "interference coordination random clustering and adaptive limited feedback"
    - "five disruptive technology directions for 5g"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "fundamental limits of cooperation"
    - "downlink sdma with limited feedback in interference limited wireless networks"
    - "base station cooperation with feedback optimization a large system analysis"

Connected through hyperedge HE-6 (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-7 (induced by source paper "a graph neural network approach for scalable wireless power control"):
  with members:
    - "learning to branch accelerating resource allocation in wireless networks"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "the roadmap to 6g ai empowered wireless networks"
    - "a comprehensive survey on graph neural networks"
    - "learning to optimize training deep neural networks for interference management"
    - "graph convolutional neural networks for web scale recommender systems"
    - "model aided wireless artificial intelligence embedding expert knowledge in deep neural networks towards wireless systems optimization"
    - "combinatorial optimization with graph convolutional networks and guided tree search"

Connected through hyperedge HE-8 (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"
    - "energy efficiency of downlink transmission strategies for cloud 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"

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)