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: "optimal power allocation for distributed detection over mimo channels in wireless sensor networks"
  Abstract: In distributed detection systems with wireless sensor networks, the communication between sensors and a fusion center is not perfect due to interference and limited transmitter power at the sensors to combat noise at the fusion center's receiver. The problem of optimizing detection performance with such imperfect communication brings a new challenge to distributed detection. In this paper, sensors are assumed to have independent but nonidentically distributed observations, and a multiple-input/multiple-output (MIMO) channel model is included to account for imperfect communication between the sensors and the fusion center. The J-divergence between the distributions of the detection statistic under different hypotheses is used as a performance criterion in order to provide a tractable analysis. Optimization of performance with individual and total transmitter power constraints on the sensors is studied, and the corresponding power allocation scheme strikes a tradeoff between two factors, the communication channel quality and the local decision quality. For the case with orthogonal channels, under certain conditions, the power allocation can be solved by a weighted water-filling al.gorithm. Simulations show that the proposed power allocation in certain cases only consumes as little as 25% of the total power used by an equal power allocation scheme while achieving the same performance.

Connected through hyperedge HE-1 (induced by source paper "rician mimo channel and jamming aware decision fusion"):
  with members:
    - "decision fusion with unknown sensor detection probability"
    - "massive mimo for wireless sensing with a coherent multiple access channel"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "distributed optimal quantization and power allocation for sensor detection via consensus"):
  with members:
    - "distributed detection and estimation in wireless sensor networks"
    - "quantized fusion rules for energy based distributed detection in wireless sensor networks"
    - "distributed estimation and control of algebraic connectivity over random graphs"

Connected through hyperedge HE-3 (induced by source paper "performance analysis and design of maximum ratio combining in channel aware mimo decision fusion"):
  with members:
    - "optimality of received energy in decision fusion over rayleigh fading diversity mac with non identical sensors"

Connected through hyperedge HE-4 (induced by source paper "optimality of received energy in decision fusion over rayleigh fading diversity mac with non identical sensors"):
  with members:
    - "distributed detection over fading macs with multiple antennas at the fusion center"

Connected through hyperedge HE-5 (induced by source paper "distributed detection over fading macs with multiple antennas at the fusion center"):
  with members:
    - "large system decentralized detection performance under communication 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)