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: "massive mimo for wireless sensing with a coherent multiple access channel"
  Abstract: We consider the detection and estimation of a zero-mean Gaussian signal in a wireless sensor network with a coherent multiple access channel, when the fusion center (FC) is configured with a large number of antennas and the wireless channels between the sensor nodes and FC experience Rayleigh fading. For the detection problem, we study the Neyman-Pearson (NP) Detector and Energy Detector (ED), and find optimal values for the sensor transmission gains. For the NP detector which requires channel state information (CSI), we show that detection performance remains asymptotically constant with the number of FC antennas if the sensor transmit power decreases proportionally with the increase in the number of antennas. Performance bounds show that the benefit of multiple antennas at the FC disappears as the transmit power grows. The results of the NP detector are also generalized to the linear minimum mean squared error estimator. For the ED which does not require CSI, we derive optimal gains that maximize the deflection coefficient of the detector, and we show that a constant deflection can be asymptotically achieved if the sensor transmit power scales as the inverse square root of the number of FC antennas. Unlike the NP detector, for high sensor power the multi-antenna ED is observed to empirically have significantly better performance than the single-antenna implementation. A number of simulation results are included to validate the analysis.

Connected through hyperedge HE-1 (induced by source paper "on throughput of compressive random access for one short message delivery in iot"):
  with members:
    - "massive mimo has unlimited capacity"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "device activity and embedded information bit detection using amp in massive mimo"
    - "random pilot and data access in massive mimo for machine type communications"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "asynchronous code division random access using convex optimization"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-2 (induced by source paper "optimized transmission for consensus in wireless sensor networks"):
  with members:
    - "optimal power allocation for parameter tracking in a distributed amplify and forward sensor network"
    - "designing unimodular codes via quadratic optimization"
    - "estimation in phase shift and forward wireless sensor networks"
    - "optimal relay functionality for snr maximization in memoryless relay networks"

Connected through hyperedge HE-3 (induced by source paper "on the feasibility of full duplex large scale mimo cellular systems"):
  with members:
    - "full duplex cloud radio access network stochastic design and analysis"
    - "multi cell massive mimo beamforming in assuring qos for large numbers of users"
    - "compact full duplex mimo radios in d2d underlaid cellular networks from system design to prototype results"
    - "massive mimo ten myths and one critical question"
    - "performance analysis of massive mimo for cell boundary users"
    - "prototyping real time full duplex radios"
    - "compressed channel feedback for correlated massive mimo systems"
    - "performance analysis of self interference cancellation methods in full duplex large scale mimo systems"

Connected through hyperedge HE-4 (induced by source paper "wireless power transfer for distributed estimation in sensor networks"):
  with members:
    - "source channel coding under energy delay and buffer constraints"
    - "secrecy wireless information and power transfer with miso beamforming"
    - "multiuser miso beamforming for simultaneous wireless information and power transfer"
    - "joint transmit beamforming and receive power splitting for miso swipt systems"
    - "wireless information and energy transfer in multi antenna interference channel"
    - "optimal power allocation for parameter tracking in a distributed amplify and forward sensor network"
    - "throughput maximization in wireless powered communication networks"
    - "multi antenna wireless powered communication with energy beamforming"

Connected through hyperedge HE-5 (induced by source paper "rician mimo channel and jamming aware decision fusion"):
  with members:
    - "decision fusion with unknown sensor detection probability"
    - "performance analysis and design of maximum ratio combining in channel aware mimo decision fusion"
    - "optimality of received energy in decision fusion over rayleigh fading diversity mac with non identical sensors"
    - "optimal power allocation for distributed detection over mimo channels in wireless sensor networks"

Connected through hyperedge HE-6 (induced by source paper "on m ary distributed detection for power constraint wireless sensor networks"):
  with members:
    - "distributed binary detection over fading channels cooperative and parallel architectures"
    - "design of binary quantizers for distributed detection under secrecy constraints"

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)