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 selection for event detection in wireless sensor networks"
  Abstract: We consider the problem of sensor selection for event detection in wireless sensor networks (WSNs). We want to choose a subset of p out of n sensors that yields the best detection performance. As the sensor selection optimality criteria, we propose the Kullback-Leibler and Chernoff distances between the distributions of the selected measurements under the two hypothesis. We formulate the maxmin robust sensor selection problem to cope with the uncertainties in distribution means. We prove that the sensor selection problem is NP hard, for both Kullback-Leibler and Chernoff criteria. To (sub)optimally solve the sensor selection problem, we propose an algorithm of affordable complexity. Extensive numerical simulations on moderate size problem instances (when the optimum by exhaustive search is feasible to compute) demonstrate the algorithm's near optimality in a very large portion of problem instances. For larger problems, extensive simulations demonstrate that our algorithm outperforms random searches, once an upper bound on computational time is set. We corroborate numerically the validity of the Kullback-Leibler and Chernoff sensor selection criteria, by showing that they lead to sensor selections nearly optimal both in the Neyman-Pearson and Bayes sense.

Connected through hyperedge HE-1 (induced by source paper "random interruptions in cooperation for spectrum sensing in cognitive radio networks"):
  with members:
    - "censored truncated sequential spectrum sensing for cognitive radio networks"
    - "green cellular networks a survey some research issues and challenges"
    - "optimal multiband joint detection for spectrum sensing in cognitive radio networks"

Connected through hyperedge HE-2 (induced by source paper "measurement partitioning and observational equivalence in state estimation"):
  with members:
    - "graph theoretic distributed inference in social networks"
    - "on the genericity properties in distributed estimation topology design and sensor placement"

Connected through hyperedge HE-3 (induced by source paper "sparsity promoting sensor selection for non linear measurement models"):
  with members:
    - "near optimal sensor placement for linear inverse problems"
    - "sparsity exploiting anchor placement for localization in sensor networks"
    - "dynamic network cartography advances in network health monitoring"

Connected through hyperedge HE-4 (induced by source paper "large deviations performance of consensus innovations distributed detection with non gaussian observations"):
  with members:
    - "distributed detection over noisy networks large deviations analysis"
    - "optimal precoding design and power allocation for decentralized detection of deterministic signals"
    - "distributed detection over time varying networks large deviations analysis"
    - "weight optimization for consensus algorithms with correlated switching topology"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"
    - "topology for distributed inference on graphs"

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)