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: "precoding for multiuser mimo systems with single fed parasitic antenna arrays"
  Abstract: Transmitter (TX) cooperation at various levels has been shown to increase the sum throughput of multiuser multiple-input multiple-output (MIMO) systems. In this paper we consider a K-user MIMO system where TXs have only global channel state knowledge. It has been theoretically shown that interference alignment (IA) achieves the K/2 degrees of freedom of this K-user MIMO interference channel. However, results on IA and all proposed transceiver techniques for this channel up to date, assume conventional antenna arrays at the transceivers with multiple radio-frequency (RF) chains, each connected to a different antenna element. To reduce the consequent hardware burden and power dissipation imposed by such arrays, we propose in this paper the utilization of compact single-RF electronically steerable parasitic (passive) array radiators (ESPARs) at the cooperating TXs. A signal model capable of capturing the characteristics of the considered antenna arrays is first described and then a general precoding design methodology for the tunable parasitic loads at the TXs' ESPARs is introduced. Specific precoding techniques and an indicative ESPAR design are presented for a 3-user 2x2 MIMO system with one ESPAR TX, and the obtained performance evaluation results show that the gains of TX cooperation are still feasible.

Connected through hyperedge HE-1 (induced by source paper "reconfigurable intelligent surfaces for energy efficiency in wireless communication"):
  with members:
    - "energy efficient multi user miso communication using low resolution large intelligent surfaces"
    - "achievable rate maximization by passive intelligent mirrors"
    - "using any surface to realize a new paradigm for wireless communications"
    - "energy efficiency of mmwave massive mimo precoding with low resolution dacs"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "capacity degradation with modeling hardware impairment in large intelligent surface"
    - "asymptotically optimal estimation algorithm for the sparse signal with arbitrary distributions"

Connected through hyperedge HE-2 (induced by source paper "energy efficient multi user miso communication using low resolution large intelligent surfaces"):
  with members:
    - "achievable rate maximization by passive intelligent mirrors"
    - "using any surface to realize a new paradigm for wireless communications"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "the potential of using large antenna arrays on intelligent surfaces"
    - "position aided beam alignment for millimeter wave backhaul systems with large phased arrays"
    - "advanced coordinated beamforming for the downlink of future lte cellular networks"
    - "energy efficient power control a look at 5g wireless technologies"
    - "a survey of energy efficient techniques for 5g networks and challenges ahead"

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)