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 "multihop backhaul compression for the uplink of cloud radio access networks"):
  Abstract: In cloud radio access networks (C-RANs), the baseband processing of the radio units (RUs) is migrated to remote control units (CUs). This is made possible by a network of backhaul links that connects RUs and CUs and that carries compressed baseband signals. While prior work has focused mostly on single-hop backhaul networks, this paper investigates efficient backhaul compression strategies for the uplink of C-RANs with a general multihop backhaul topology. A baseline multiplex-and-forward (MF) scheme is first studied in which each RU forwards the bit streams received from the connected RUs without any processing. It is observed that this strategy may cause significant performance degradation in the presence of a dense deployment of RUs with a well connected backhaul network. To obviate this problem, a scheme is proposed in which each RU decompresses the received bit streams and performs linear in-network processing of the decompressed signals. For both the MF and the decompress-process-and-recompress (DPR) backhaul schemes, the optimal design is addressed with the aim of maximizing the sum-rate under the backhaul capacity constraints. Recognizing the significant demands of the optimal solution of the DPR scheme in terms of channel state information (CSI) at the RUs, decentralized optimization algorithms are proposed under the assumption of limited CSI at the RUs. Numerical results are provided to compare the performance of the MF and DPR schemes, highlighting the potential advantage of in-network processing and the impact of CSI limitations.

Includes members:
  Member "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "caching at base stations with multi cluster multicast wireless backhaul via accelerated first order algorithm")
  Member "millimeter wave beamforming for wireless backhaul and access in small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning beam codebooks with neural networks towards environment aware mmwave mimo")
  Member "uplink multicell processing with limited backhaul via per base station successive interference cancellation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "integer forcing architectures for uplink cloud radio access networks")
  Member "reduced dimension linear transform coding of correlated signals in networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "index coding and network coding via rank minimization")
  Member "robust and efficient distributed compression for cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "integer forcing architectures for uplink cloud radio access networks")
  Member "cascade multiterminal source coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hypothesis testing over the two hop relay network")
  Member "interference mitigation through limited receiver cooperation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "how much can d2d communication reduce content delivery latency in fog networks with edge caching")
  Member "approximate capacity of gaussian relay networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "divide and conquer approaching the capacity of the two pair bidirectional gaussian relay network")

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)