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: "receive combining vs multi stream multiplexing in downlink systems with multi antenna users"
  Abstract: In downlink multi-antenna systems with many users, the multiplexing gain is strictly limited by the number of transmit antennas N and the use of these antennas. Assuming that the total number of receive antennas at the multi-antenna users is much larger than N, the maximal multiplexing gain can be achieved with many different transmission/reception strategies. For example, the excess number of receive antennas can be utilized to schedule users with effective channels that are near-orthogonal, for multi-stream multiplexing to users with well-conditioned channels, and/or to enable interference-aware receive combining. In this paper, we try to answer the question if the N data streams should be divided among few users (many streams per user) or many users (few streams per user, enabling receive combining). Analytic results are derived to show how user selection, spatial correlation, heterogeneous user conditions, and imperfect channel acquisition (quantization or estimation errors) affect the performance when sending the maximal number of streams or one stream per scheduled user-the two extremes in data stream allocation. While contradicting observations on this topic have been reported in prior works, we show that selecting many users and allocating one stream per user (i.e., exploiting receive combining) is the best candidate under realistic conditions. This is explained by the provably stronger resilience towards spatial correlation and the larger benefit from multi-user diversity. This fundamental result has positive implications for the design of downlink systems as it reduces the hardware requirements at the user devices and simplifies the throughput optimization.

Connected through hyperedge HE-1 (induced by source paper "an overview on resource allocation techniques for multi user mimo systems"):
  with members:
    - "low complexity downlink user selection for massive mimo systems"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "user association in 5g networks a survey and an outlook"
    - "cell free massive mimo versus small cells"
    - "mu mimo mac protocols for wireless local area networks a survey"
    - "on the number of rf chains and phase shifters and scheduling design with hybrid analog digital beamforming"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"

Connected through hyperedge HE-2 (induced by source paper "symbol level and multicast precoding for multiuser multiantenna downlink a survey classification and challenges"):
  with members:
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "an overview on resource allocation techniques for multi user mimo systems"
    - "spatio temporal waveform design for multiuser massive mimo downlink with 1 bit receivers"
    - "joint beamforming and broadcasting in massive mimo"
    - "coordinate update algorithms for robust power loading for the mu miso downlink with outage constraints"
    - "energy efficient symbol level precoding in multiuser miso based on relaxed detection region"
    - "robust transmission in downlink multiuser miso systems a rate splitting approach"
    - "adaptive pilot clustering in heterogeneous massive mimo networks"

Connected through hyperedge HE-3 (induced by source paper "massive mimo unlicensed for high performance indoor networks"):
  with members:
    - "cell edge aware precoding for downlink massive mimo cellular networks"
    - "enhancing coexistence in the unlicensed band with massive mimo"
    - "massive mimo unlicensed a new approach to dynamic spectrum access"
    - "operating massive mimo in unlicensed bands for enhanced coexistence and spatial reuse"

Connected through hyperedge HE-4 (induced by source paper "massive mimo with multi antenna users when are additional user antennas beneficial"):
  with members:
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-5 (induced by source paper "spatial dct based channel estimation in multi antenna multi cell interference channels"):
  with members:
    - "joint channel estimation and pilot allocation in underlay cognitive miso networks"
    - "training sequence design for mimo channels an application oriented approach"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"
    - "cellular systems with many antennas large system analysis under pilot contamination"
    - "adaptive spatial intercell interference cancellation in multicell wireless networks"
    - "pilot contamination and precoding in multi cell tdd systems"
    - "mimo broadcast channels with finite rate feedback"

Connected through hyperedge HE-6 (induced by source paper "distributed bi directional training of nonlinear precoders and receivers in cellular networks"):
  with members:
    - "efficient solutions for weighted sum rate maximization in multicellular networks with channel uncertainties"
    - "downlink interference alignment"
    - "pilot contamination and precoding in multi cell tdd systems"

Connected through hyperedge HE-7 (induced by source paper "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"):
  with members:
    - "uplink performance of time reversal mrc in massive mimo systems subject to phase noise"
    - "robust estimates of covariance matrices in the large dimensional regime"
    - "low complexity polynomial channel estimation in large scale mimo with arbitrary statistics"
    - "blind pilot decontamination"
    - "massive mimo and small cells improving energy efficiency by optimal soft cell coordination"
    - "capacity limits and multiplexing gains of mimo channels with transceiver impairments"
    - "hardware impairments in large scale miso systems energy efficiency estimation and capacity limits"
    - "linear precoding based on polynomial expansion reducing complexity in massive mimo"

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)