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: "distributed fronthaul compression and joint signal recovery in cloud ran"
  Abstract: The cloud radio access network (C-RAN) is a promising network architecture for future mobile communications, and one practical hurdle for its large scale implementation is the stringent requirement of high capacity and low latency fronthaul connecting the distributed remote radio heads (RRH) to the centralized baseband pools (BBUs) in the C-RAN. To improve the scalability of C-RAN networks, it is very important to take the fronthaul loading into consideration in the signal detection, and it is very desirable to reduce the fronthaul loading in C-RAN systems. In this paper, we consider uplink C-RAN systems and we propose a distributed fronthaul compression scheme at the distributed RRHs and a joint recovery algorithm at the BBUs by deploying the techniques of distributed compressive sensing (CS). Different from conventional distributed CS, the CS problem in C-RAN system needs to incorporate the underlying effect of multi-access fading for the end-to-end recovery of the transmitted signals from the users. We analyze the performance of the proposed end-to-end signal recovery algorithm and we show that the aggregate measurement matrix in C-RAN systems, which contains both the distributed fronthaul compression and multiaccess fading, can still satisfy the restricted isometry property with high probability. Based on these results, we derive tradeoff results between the uplink capacity and the fronthaul loading in C-RAN systems.

Connected through hyperedge HE-1 (induced by source paper "joint energy minimization and resource allocation in c ran with mobile cloud"):
  with members:
    - "decentralized computation offloading game for mobile cloud computing"
    - "aqua computing coupling computing and communications"
    - "joint optimization of radio and computational resources for multicell mobile edge computing"
    - "what will 5g be"
    - "sparse beamforming and user centric clustering for downlink cloud radio access network"

Connected through hyperedge HE-2 (induced by source paper "unified offloading decision making and resource allocation in me ran"):
  with members:
    - "joint energy minimization and resource allocation in c ran with mobile cloud"
    - "joint precoding and rrh selection for user centric green mimo c ran"
    - "computation diversity in emerging networking paradigms"
    - "decentralized computation offloading game for mobile cloud computing"

Connected through hyperedge HE-3 (induced by source paper "recent advances in cloud radio access networks system architectures key techniques and open issues"):
  with members:
    - "fronthaul compression and precoding design for c rans over ergodic fading channels"
    - "optimal fronthaul quantization for cloud radio positioning"
    - "inter tier interference suppression in heterogeneous cloud radio access networks"
    - "contract based interference coordination in heterogeneous cloud radio access networks"
    - "are heterogeneous cloud based radio access networks cost effective"
    - "energy efficient resource assignment and power allocation in heterogeneous cloud radio access networks"
    - "cooperative interference mitigation and handover management for heterogeneous cloud small cell networks"
    - "fronthaul constrained cloud radio access networks insights and challenges"

Connected through hyperedge HE-4 (induced by source paper "fronthaul compression and transmit beamforming optimization for multi antenna uplink c ran"):
  with members:
    - "fog computing based radio access networks issues and challenges"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "optimal fronthaul compression for synchronization in the uplink of cloud radio access networks"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"
    - "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies"
    - "optimized backhaul compression for uplink cloud radio access network"
    - "sparse beamforming and user centric clustering for downlink cloud radio access network"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"

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)