human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "energy efficiency of downlink transmission strategies for cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse optimization for green edge ai inference")
  Member "cloud radio access network virtualizing wireless access for dense heterogeneous systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint 3d maneuver and power adaptation for secure uav communication with comp reception")
  Member "sparse beamforming and user centric clustering for downlink cloud radio access network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure millimeter wave cloud radio access networks relying on microwave multicast fronthaul")
  Member "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "caching at base stations with multi cluster multicast wireless backhaul via accelerated first order algorithm")
  Member "min flow rate maximization for software defined radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wireless communications in the era of big data")
  Member "multihop backhaul compression for the uplink of cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust regularized zf in cooperative broadcast channel under distributed csit")
  Member "optimized data sharing in multicell mimo with finite backhaul capacity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "interference mitigation via rate splitting and common message decoding in cloud radio access networks")

Question: Please tell me which class the source paper 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)