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 mimo receiver achievable rates and upper bounds"
  Abstract: In this paper we investigate the achievable rate of a system that includes a nomadic transmitter with several antennas, which is received by multiple agents, exhibiting independent channel gains and additive circular-symmetric complex Gaussian noise. In the nomadic regime, we assume that the agents do not have any decoding ability. These agents process their channel observations and forward them to the final destination through lossless links with a fixed capacity. We propose new achievable rates based on elementary compression and also on a Wyner-Ziv (CEO-like) processing, for both fast fading and block fading channels, as well as for general discrete channels. The simpler two agents scheme is solved, up to an implicit equation with a single variable. Limiting the nomadic transmitter to a circular-symmetric complex Gaussian signalling, new upper bounds are derived for both fast and block fading, based on the vector version of the entropy power inequality. These bounds are then compared to the achievable rates in several extreme scenarios. The asymptotic setting with numbers of agents and transmitter's antennas taken to infinity is analyzed. In addition, the upper bounds are analytically shown to be tight in several examples, while numerical calculations reveal a rather small gap in a finite $2\times2$ setting. The advantage of the Wyner-Ziv approach over elementary compression is shown where only the former can achieve the full diversity-multiplexing tradeoff. We also consider the non-nomadic setting, with agents that can decode. Here we give an achievable rate, over fast fading channel, which combines broadcast with dirty paper coding and the decentralized reception, which was introduced for the nomadic setting.

Connected through hyperedge HE-1 (induced by source paper "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"):
  with members:
    - "fronthaul compression and transmit beamforming optimization for multi antenna uplink c ran"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"
    - "optimized backhaul compression for uplink cloud radio access network"
    - "short message noisy network coding with a decode forward option"
    - "uplink multicell processing with limited backhaul via per base station successive interference cancellation"
    - "an outer bound for the vector gaussian ceo problem"
    - "robust and efficient distributed compression for cloud radio access networks"
    - "wireless network information flow a deterministic approach"

Connected through hyperedge HE-2 (induced by source paper "cluster content caching an energy efficient approach to improve quality of service in cloud radio access networks"):
  with members:
    - "fog computing based radio access networks issues and challenges"
    - "inter tier interference suppression in heterogeneous cloud radio access networks"
    - "are heterogeneous cloud based radio access networks cost effective"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"
    - "on content centric wireless delivery networks"
    - "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies"
    - "optimized backhaul compression for uplink cloud radio access network"

Connected through hyperedge HE-3 (induced by source paper "oblivious fronthaul constrained relay for a gaussian channel"):
  with members:
    - "fundamental distortion limits of analog to digital compression"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "oblivious processing in a fronthaul constrained gaussian channel"
    - "remarks on the most informative function conjecture at fixed mean"
    - "lower bounds and approximations for the information rate of the isi channel"
    - "on the capacity of the dither quantized gaussian channel"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "capacity of the discrete time awgn channel under output quantization"

Connected through hyperedge HE-4 (induced by source paper "optimized uplink transmission in multi antenna c ran with spatial compression and forward"):
  with members:
    - "optimized backhaul compression for uplink cloud radio access network"
    - "joint power control and fronthaul rate allocation for throughput maximization in ofdma based cloud radio access network"
    - "uplink multicell processing with limited backhaul via per base station successive interference cancellation"
    - "robust and efficient distributed compression for cloud radio access networks"
    - "wireless network information flow a deterministic approach"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-5 (induced by source paper "oblivious processing in a fronthaul constrained gaussian channel"):
  with members:
    - "sampling stationary signals subject to bitrate constraints"
    - "distortion rate function of sub nyquist sampled gaussian sources"

Connected through hyperedge HE-6 (induced by source paper "optimized backhaul compression for uplink cloud radio access network"):
  with members:
    - "uplink multicell processing with limited backhaul via per base station successive interference cancellation"
    - "robust and efficient distributed compression for cloud radio access networks"
    - "decomposition by successive convex approximation a unifying approach for linear transceiver design in heterogeneous networks"
    - "compute and forward strategies for cooperative distributed antenna systems"
    - "wireless network information flow a deterministic approach"
    - "slepian wolf coding over cooperative relay networks"
    - "uplink comp under a constrained backhaul and imperfect channel knowledge"

Connected through hyperedge HE-7 (induced by source paper "joint power control and fronthaul rate allocation for throughput maximization in ofdma based cloud radio access network"):
  with members:
    - "optimized backhaul compression for uplink cloud radio access network"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "uplink multicell processing with limited backhaul via per base station successive interference cancellation"
    - "dynamic resource allocation in cognitive radio networks"

Connected through hyperedge HE-8 (induced by source paper "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"):
  with members:
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "reverse compute and forward a low complexity architecture for downlink distributed antenna systems"
    - "distributed robust multicell coordinated beamforming with imperfect csi an admm approach"
    - "robust and efficient distributed compression for cloud radio access networks"
    - "decomposition by successive convex approximation a unifying approach for linear transceiver design in heterogeneous networks"
    - "compute and forward strategies for cooperative distributed antenna systems"
    - "optimized data sharing in multicell mimo with finite backhaul capacity"
    - "quantized compute and forward a low complexity architecture for distributed antenna 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)