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: "optimal channel training in uplink network mimo systems"
  Abstract: We consider a multicell frequency-selective fading uplink channel (network MIMO) from K single-antenna user terminals (UTs) to B cooperative base stations (BSs) with M antennas each. The BSs, assumed to be oblivious of the applied codebooks, forward compressed versions of their observations to a central station (CS) via capacity limited backhaul links. The CS jointly decodes the messages from all UTs. Since the BSs and the CS are assumed to have no prior channel state information (CSI), the channel needs to be estimated during its coherence time. Based on a lower bound of the ergodic mutual information, we determine the optimal fraction of the coherence time used for channel training, taking different path losses between the UTs and the BSs into account. We then study how the optimal training length is impacted by the backhaul capacity. Although our analytical results are based on a large system limit, we show by simulations that they provide very accurate approximations for even small system dimensions.

Connected through hyperedge HE-1 (induced by source paper "uplink channel estimation and data transmission in millimeter wave cran with lens antenna arrays"):
  with members:
    - "fronthaul limited uplink ofdma in ultra dense cran with hybrid decoding"
    - "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran"
    - "multi user millimeter wave mimo with full dimensional lens antenna array"
    - "rf lens embedded massive mimo systems fabrication issues and codebook design"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "cost effective millimeter wave communications with lens antenna array"
    - "millimeter wave mimo with lens antenna array a new path division multiplexing paradigm"
    - "optimized uplink transmission in multi antenna c ran with spatial compression and forward"

Connected through hyperedge HE-2 (induced by source paper "lmmse receivers in uplink massive mimo systems with correlated rician fading"):
  with members:
    - "massive mimo with spatially correlated rician fading channels"
    - "line of sight and pilot contamination effects on correlated multi cell massive mimo systems"
    - "impact of line of sight and unequal spatial correlation on uplink mu mimo systems"
    - "asymptotic analysis of multicell massive mimo over rician fading channels"
    - "achievable rate of rician large scale mimo channels with transceiver hardware impairments"
    - "power scaling of uplink massive mimo systems with arbitrary rank channel means"
    - "what will 5g be"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"

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 "selective uplink training for massive mimo systems"):
  with members:
    - "massive mimo ten myths and one critical question"
    - "high dimensional csi acquisition in massive mimo sparsity inspired approaches"
    - "massive mimo for next generation wireless systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "achieving massive mimo spectral efficiency with a not so large number of antennas"
    - "multiuser mimo achievable rates with downlink training and channel state feedback"

Connected through hyperedge HE-5 (induced by source paper "cloud radio access networks uplink channel estimation and downlink precoding"):
  with members:
    - "fronthaul compression and precoding design for c rans over ergodic fading channels"
    - "expanding the compute and forward framework unequal powers signal levels and multiple linear combinations"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"

Connected through hyperedge HE-6 (induced by source paper "fronthaul compression and precoding design for c rans over ergodic fading channels"):
  with members:
    - "joint power control and fronthaul rate allocation for throughput maximization in ofdma based cloud radio access network"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"

Connected through hyperedge HE-7 (induced by source paper "impact of residual transmit rf impairments on training based mimo systems"):
  with members:
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "on the mimo capacity with residual transceiver hardware impairments"
    - "capacity bounds for mimo microwave backhaul links affected by phase noise"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "on the impact of hardware impairments on massive mimo"
    - "two way relaying under the presence of relay transceiver hardware impairments"
    - "capacity limits and multiplexing gains of mimo channels with transceiver impairments"

Connected through hyperedge HE-8 (induced by source paper "channel estimation in massive mimo systems"):
  with members:
    - "uplink interference reduction in large scale antenna systems"
    - "dealing with interference in distributed large scale mimo systems a statistical approach"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "hierarchical interference mitigation for massive mimo cellular networks"
    - "blind pilot decontamination"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "a coordinated approach to channel estimation in large scale multiple 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)