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: "sensor configuration and activation for field detection in large sensor arrays"
  Abstract: The problems of sensor configuration and activation for the detection of correlated random fields using large sensor arrays are considered. Using results that characterize the large-array performance of sensor networks in this application, the detection capabilities of different sensor configurations are analyzed and compared. The dependence of the optimal choice of configuration on parameters such as sensor signal-to-noise ratio (SNR), field correlation, etc., is examined, yielding insights into the most effective choices for sensor selection and activation in various operating regimes.

Connected through hyperedge HE-1 (induced by source paper "energy scaling laws for distributed inference in random fusion networks"):
  with members:
    - "detection of gauss markov random fields with nearest neighbor dependency"
    - "information energy and density for ad hoc sensor networks over correlated random fields large deviations analysis"

Connected through hyperedge HE-2 (induced by source paper "information energy and density for ad hoc sensor networks over correlated random fields large deviations analysis"):
  with members:
    - "how much information can one get from a wireless ad hoc sensor network over a correlated random field"
    - "large deviations analysis for the detection of 2d hidden gauss markov random fields using sensor networks"
    - "neyman pearson detection of gauss markov signals in noise closed form error exponentand properties"

Connected through hyperedge HE-3 (induced by source paper "large deviations analysis for the detection of 2d hidden gauss markov random fields using sensor networks"):
  with members:
    - "neyman pearson detection of gauss markov signals in noise closed form error exponentand properties"

Connected through hyperedge HE-4 (induced by source paper "optimal and suboptimal detection of gaussian signals in noise asymptotic relative efficiency"):
  with members:
    - "a large deviations approach to sensor scheduling for detection of correlated random fields"

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)