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: "optimized uplink transmission in multi antenna c ran with spatial compression and forward"
  Abstract: MIMO and cloud radio access network (C-RAN) are promising techniques for implementing future wireless communication systems, where a large number of antennas are deployed either being co-located at the base station or totally distributed at separate sites called remote radio heads (RRHs), both to achieve enormous spectrum efficiency and energy efficiency gains. Here, we consider a general antenna deployment method for wireless networks, termed multi-antenna C-RAN, where a flexible number of antennas can be equipped at each RRH to more effectively balance the performance and fronthaul complexity tradeoff beyond the conventional massive MIMO and single-antenna C-RAN. To coordinate and control the fronthaul traffic over multi-antenna RRHs, under the uplink communication setup, we propose a new “spatial-compression-and-forward (SCF)” scheme, where each RRH first performs a linear spatial filtering to denoise and maximally compress its received signals from multiple users to a reduced number of dimensions, then conducts uniform scalar quantization over each of the resulting dimensions in parallel, and finally sends the total quantized bits via a finite-rate fronthaul link to the baseband unit (BBU) for joint information decoding. Under this scheme, we maximize the minimum SINR of all users at the BBU by a joint resource allocation over the wireless transmission and fronthaul links. Specifically, each RRH determines its own spatial filtering solution in a distributed manner to reduce the signaling overhead with the BBU, while the BBU jointly optimizes the users’ transmit power, the RRHs’ fronthaul bits allocation, and the BBU’s receive beamforming with fixed spatial filters at individual RRHs. Numerical results show that, given a total number of antennas to be deployed, multi-antenna C-RAN with the proposed SCF and joint optimization significantly outperforms both massive MIMO and single-antenna C-RAN under practical fronthaul capacity constraints.

Connected through hyperedge HE-1 (induced by source paper "computation offloading for iot in c ran optimization and deep learning"):
  with members:
    - "non stationarities in extra large scale massive mimo"
    - "cross layer design for mission critical iot in mobile edge computing systems"
    - "two timescale hybrid compression and forward for massive mimo aided c ran"
    - "learning to optimize training deep neural networks for interference management"
    - "beyond massive mimo the potential of positioning with large intelligent surfaces"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "performance analysis of large intelligence surfaces liss asymptotic data rate and channel hardening effects"
    - "deep learning in mobile and wireless networking a survey"

Connected through hyperedge HE-2 (induced by source paper "two timescale hybrid compression and forward for massive mimo aided c ran"):
  with members:
    - "a parallel decomposition method for nonconvex stochastic multi agent optimization problems"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-3 (induced by source paper "optimal discrete spatial compression for beamspace massive mimo signals"):
  with members:
    - "a real time millimeter wave phased array mimo channel sounder"
    - "branch and bound precoding for multiuser mimo systems with 1 bit quantization"
    - "spatio temporal waveform design for multiuser massive mimo downlink with 1 bit receivers"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "cost effective millimeter wave communications with lens antenna array"
    - "fronthaul compression and transmit beamforming optimization for multi antenna uplink c ran"
    - "quantized precoding for massive mu mimo"
    - "hybrid beamforming for massive mimo a survey"

Connected through hyperedge HE-4 (induced by source paper "uplink channel estimation and data transmission in millimeter wave cran with lens antenna arrays"):
  with members:
    - "fronthaul limited uplink ofdma in ultra dense cran with hybrid decoding"
    - "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran"
    - "multi user millimeter wave mimo with full dimensional lens antenna array"
    - "rf lens embedded massive mimo systems fabrication issues and codebook design"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "cost effective millimeter wave communications with lens antenna array"
    - "millimeter wave mimo with lens antenna array a new path division multiplexing paradigm"
    - "optimized backhaul compression for uplink cloud radio access network"

Connected through hyperedge HE-5 (induced by source paper "multi beam uav communication in cellular uplink cooperative interference cancellation and sum rate maximization"):
  with members:
    - "mobile networks connected drones field trials simulations and design insights"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"
    - "massive mimo for communications with drone swarms"
    - "massive mimo for drone communications applications case studies and future directions"
    - "a survey on non orthogonal multiple access for 5g networks research challenges and future trends"
    - "coexistence of terrestrial and aerial users in cellular networks"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"

Connected through hyperedge HE-6 (induced by source paper "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran"):
  with members:
    - "locally orthogonal training design for cloud rans based on graph coloring"
    - "millimeter wave wireless communications new results for rural connectivity"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "wireless communications in the era of big data"
    - "5g ultra dense cellular networks"
    - "large scale convex optimization for ultra dense cloud ran"
    - "optimized backhaul compression for uplink cloud radio access network"
    - "dynamic nested clustering for parallel phy layer processing in cloud rans"

Connected through hyperedge HE-7 (induced by source paper "fronthaul limited uplink ofdma in ultra dense cran with hybrid decoding"):
  with members:
    - "cross layer design for downlink multihop cloud radio access networks with network coding"
    - "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran"
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "green ofdma resource allocation in cache enabled cran"
    - "wireless communications in the era of big data"
    - "large scale convex optimization for ultra dense cloud ran"
    - "optimized backhaul compression for uplink cloud radio access network"

Connected through hyperedge HE-8 (induced by source paper "hybrid precoding for massive mimo systems in cloud ran architecture with capacity limited fronthauls"):
  with members:
    - "layered downlink precoding for c ran systems with full dimensional mimo"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "recent advances in cloud radio access networks system architectures key techniques and open issues"
    - "on the number of rf chains and phase shifters and scheduling design with hybrid analog digital beamforming"
    - "channel covariance estimation in massive mimo frequency division duplex systems"
    - "hybrid beamforming for massive mimo a survey"
    - "regularized zf in cooperative broadcast channels under distributed csit a large system analysis"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"

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)