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: "cross layer design for downlink multihop cloud radio access networks with network coding"
  Abstract: There are two fundamentally different fronthaul techniques in the downlink communication of cloud radio access network (C-RAN): the data-sharing strategy and the compression-based strategy. Under the former strategy, each user's message is multicast from the central processor (CP) to all the serving remote radio heads (RRHs) over the fronthaul network, which then cooperatively serve the users through joint beamforming; while under the latter strategy, the user messages are first beamformed then quantized at the CP, and the compressed signal is unicast to the corresponding RRH, which then decompresses its received signal for wireless transmission. Previous works show that in general the compression-based strategy outperforms the data-sharing strategy. This paper, on the other hand, points out that in a C-RAN model where the RRHs are connected to the CP via multihop routers, data-sharing can be superior to compression if the network coding technique is adopted for multicasting user messages to the cooperating RRHs, and the RRH's beamforming vectors, the user-RRH association, and the network coding design over the fronthaul network are jointly optimized based on the techniques of sparse optimization and successive convex approximation. This is in comparison to the compression-based strategy, where information is unicast over the fronthaul network by simple routing, and the RRH's compression noise covariance and beamforming vectors, as well as the routing strategy over the fronthaul network are jointly optimized based on the successive convex approximation technique. The observed gain in overall network throughput is due to that information multicast is more efficient than information unicast over the multihop fronthaul of a C-RAN.

Connected through hyperedge HE-1 (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"
    - "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"
    - "what will 5g be"

Connected through hyperedge HE-2 (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"
    - "analysis of massive mimo enabled downlink wireless backhauling for full duplex small cells"
    - "energy efficiency of downlink transmission strategies for cloud radio access networks"
    - "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-3 (induced by source paper "full duplex cloud radio access network stochastic design and analysis"):
  with members:
    - "self interference in full duplex multi user mimo channels"
    - "secrecy and energy efficiency in massive mimo aided heterogeneous c ran a new look at interference"
    - "echo state networks for proactive caching in cloud based radio access networks with mobile users"
    - "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"
    - "a stochastic analysis of network mimo systems"
    - "massive mimo enabled full duplex cellular networks"
    - "full duplex mimo small cell networks with interference cancellation"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"

Connected through hyperedge HE-4 (induced by source paper "fronthaul limited uplink ofdma in ultra dense cran with hybrid decoding"):
  with members:
    - "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 uplink transmission in multi antenna c ran with spatial compression and forward"
    - "optimized backhaul compression for uplink cloud radio access network"

Connected through hyperedge HE-5 (induced by source paper "multi tenant c ran with spectrum pooling downlink optimization under privacy constraints"):
  with members:
    - "optimization of massive full dimensional mimo for positioning and communication"
    - "inter operator base station coordination in spectrum shared millimeter wave cellular networks"
    - "joint base station clustering and beamforming for non orthogonal multicast and unicast transmission with backhaul constraints"
    - "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"
    - "multivariate fronthaul quantization for downlink c ran"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "robust group sparse beamforming for multicast green cloud ran with imperfect csi"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous 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)