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: "pilot signal design for massive mimo systems a received signal to noise ratio based approach"
  Abstract: In this letter, the pilot signal design for massive MIMO systems to maximize the training-based received signal-to-noise ratio (SNR) is considered under two channel models: block Gauss-Markov and block independent and identically distributed (i.i.d.) channel models. First, it is shown that under the block Gauss-Markov channel model, the optimal pilot design problem reduces to a semi-definite programming (SDP) problem, which can be solved numerically by a standard convex optimization tool. Second, under the block i.i.d. channel model, an optimal solution is obtained in closed form. Numerical results show that the proposed method yields noticeably better performance than other existing pilot design methods in terms of received SNR.

Connected through hyperedge HE-1 (induced by source paper "multi cell multiuser massive mimo networks user capacity analysis and pilot design"):
  with members:
    - "user load analysis and pilot sequence design for multi cell massive mimo networks"
    - "performance analysis and location optimization for massive mimo systems with circularly distributed antennas"
    - "graph coloring based pilot allocation to mitigate pilot contamination for multi cell massive mimo systems"
    - "power allocation schemes for multicell massive mimo systems"
    - "fundamental limits of training based multiuser mimo systems"
    - "massive mimo performance evaluation based on measured propagation data"
    - "smart pilot assignment for massive mimo"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-2 (induced by source paper "user capacity analysis and pilot design for multi cell multiuser massive mimo networks"):
  with members:
    - "user load analysis and pilot sequence design for multi cell massive mimo networks"
    - "performance analysis and location optimization for massive mimo systems with circularly distributed antennas"
    - "graph coloring based pilot allocation to mitigate pilot contamination for multi cell massive mimo systems"
    - "power allocation schemes for multicell massive mimo systems"
    - "massive mimo performance evaluation based on measured propagation data"
    - "smart pilot assignment for massive mimo"
    - "massive mimo for next generation wireless systems"
    - "blind pilot decontamination"

Connected through hyperedge HE-3 (induced by source paper "two stage beamformer design for massive mimo downlink by trace quotient formulation"):
  with members:
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "a new approach to user scheduling in massive multi user mimo broadcast channels"
    - "training sequence design for feedback assisted hybrid beamforming in massive mimo systems"
    - "two tier precoding for fdd multi cell massive mimo time varying interference networks"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "coordinated beamforming with relaxed zero forcing the sequential orthogonal projection combining method and rate control"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-4 (induced by source paper "training sequence design for feedback assisted hybrid beamforming in massive mimo systems"):
  with members:
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "a new approach to user scheduling in massive multi user mimo broadcast channels"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "noncoherent trellis coded quantization a practical limited feedback technique for massive mimo systems"
    - "joint spatial division and multiplexing opportunistic beamforming and user grouping"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-5 (induced by source paper "training beam sequence design for millimeter wave mimo systems a pomdp framework"):
  with members:
    - "pilot beam sequence design for channel estimation in millimeter wave mimo systems a pomdp framework"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "millimeter wave mimo channel tracking systems"
    - "millimeter wave beamforming for wireless backhaul and access in small cell networks"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "point based pomdp algorithms improved analysis and implementation"

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)