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: "suboptimal spatial diversity scheme for 60 ghz millimeter wave wlan"
  Abstract: This letter revisits the equal-gain (EG) spatial diversity technique, which was proposed to combat the human-induced shadowing for 60 GHz wireless local area network, under a more practical frequency-selective multi-input multi-output channel. Subsequently, a suboptimal spatial diversity scheme called maximal selection (MS) is proposed by tracing the shadowing process, owing to a considerably high data rate. Comparisons show that MS outperforms EG in terms of link margin and saves computation complexity.

Connected through hyperedge HE-1 (induced by source paper "hierarchical codebook design for beamforming training in millimeter wave communication"):
  with members:
    - "fast channel estimation for millimetre wave wireless systems using overlapped beam patterns"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"
    - "iterative eigenvalue decomposition and multipath grouping tx rx joint beamformings for millimeter wave communications"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"
    - "iterative joint beamforming training with constant amplitude phased arrays in millimeter wave communications"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "spatially sparse precoding in millimeter wave mimo systems"

Connected through hyperedge HE-2 (induced by source paper "low complexity hybrid precoding and channel estimation based on hierarchical multi beam search for millimeter wave mimo systems"):
  with members:
    - "iterative eigenvalue decomposition and multipath grouping tx rx joint beamformings for millimeter wave communications"
    - "iterative joint beamforming training with constant amplitude phased arrays in millimeter wave communications"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "millimeter wave beamforming for wireless backhaul and access in small cell networks"

Connected through hyperedge HE-3 (induced by source paper "enabling uav cellular with millimeter wave communication potentials and approaches"):
  with members:
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "millimeter wave cellular wireless networks potentials and challenges"

Connected through hyperedge HE-4 (induced by source paper "a survey of millimeter wave mmwave communications for 5g opportunities and challenges"):
  with members:
    - "hybrid millimeter wave systems a novel paradigm for hetnets"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "coverage and rate analysis for millimeter wave cellular networks"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "on the feasibility of beamforming in millimeter wave communication systems with multiple antenna arrays"
    - "millimeter wave channel modeling and cellular capacity evaluation"
    - "coding the beams improving beamforming training in mmwave communication system"

Connected through hyperedge HE-5 (induced by source paper "60 ghz mac standardization progress and way forward"):
  with members:
    - "what will 5g be"
    - "quality aware coding and relaying for 60 ghz real time wireless video broadcasting"
    - "five disruptive technology directions for 5g"
    - "wigig and ieee 802 11ad for multi gigabyte per second wpan and wlan"

Connected through hyperedge HE-6 (induced by source paper "near optimal hybrid analog and digital precoding for downlink mmwave massive mimo systems"):
  with members:
    - "low complexity near optimal signal detection for uplink large scale mimo systems"
    - "structured compressive sensing based superimposed pilot design in downlink large scale mimo systems"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-7 (induced by source paper "iterative eigenvalue decomposition and multipath grouping tx rx joint beamformings for millimeter wave communications"):
  with members:
    - "iterative joint beamforming training with constant amplitude phased arrays in millimeter wave communications"
    - "spatially sparse precoding in millimeter wave mimo 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)