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 "channel matrix sparsity with imperfect channel state information in cloud radio access networks"):
  Abstract: Channel matrix sparsification is considered as a promising approach to reduce the progressing complexity in large-scale cloud-radio access networks (C-RANs) based on ideal channel condition assumption. In this paper, the research of channel sparsification is extend to practical scenarios, in which the perfect channel state information (CSI) is not available. First, a tractable lower bound of signal-to-interferenceplus-noise ratio (SINR) fidelity, which is defined as a ratio of SINRs with and without channel sparsification, is derived to evaluate the impact of channel estimation error. Based on the theoretical results, a Dinkelbach-based algorithm is proposed to achieve the global optimal performance of channel matrix sparsification based on the criterion of distance. Finally, all these results are extended to a more challenging scenario with pilot contamination. Finally, simulation results are shown to evaluate the performance of channel matrix sparsification with imperfect CSIs and verify our analytical results.

Includes members:
  Member "energy efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep reinforcement learning based mode selection and resource management for green fog radio access networks")
  Member "cluster content caching an energy efficient approach to improve quality of service in cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "data correlation aware resource management in wireless virtual reality vr an echo state transfer learning approach")
  Member "inter tier interference suppression in heterogeneous cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "power efficient resource allocation in c rans with sinr constraints and deadlines")
  Member "fronthaul constrained cloud radio access networks insights and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the capacity regions of cloud radio access networks with limited orthogonal fronthaul")
  Member "large scale convex optimization for dense wireless cooperative networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "large scale beamforming for massive mimo via randomized sketching")
  Member "robust group sparse beamforming for multicast green cloud ran with imperfect csi":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure millimeter wave cloud radio access networks relying on microwave multicast fronthaul")
  Member "system architecture and key technologies for 5g heterogeneous cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "integer forcing architectures for uplink cloud radio access networks")
  Member "training design and channel estimation in uplink cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "comsens exploiting pilot diversity for pervasive integration of communication and sensing in mimo tdd frameworks")

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)