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: "distributed bayesian quickest change detection in sensor networks via two layer large deviation analysis"
  Abstract: We propose a distributed Bayesian quickest change detection algorithm for sensor networks, based on a random gossip inter-sensor communication structure. Without a control or fusion center, each sensor executes its local change detection procedure in a parallel and distributed fashion, interacting with its neighbor sensors via random inter-sensor communications to propagate information. By modeling the information propagation dynamics in the network as a Markov process, two-layer large deviation analysis is presented to analyze the performance of the proposed algorithm. The first-layer analysis shows that the relation between the probability of false alarm and the conditional averaged detection delay satisfies the large deviation principle, implying that the probability of false alarm according to a rare event decays to zero at an exponentially fast rate when the conditional averaged detection decay increases, where the Kullback-Leibler information number is established as a crucial factor. The second-layer analysis shows that the probability of the rare event that not all observations are available at a sensor decays to zero at an exponentially fast rate when the averaged number of communications increases, where the large deviation upper and lower bounds for this rate are also derived, based on which we show that the performance of the distributed algorithm converges exponentially fast to that of the centralized one, by proving that the defined distributed Kullback-Leibler information number converges to the centralized Kullback-Leibler information number.

Connected through hyperedge HE-1 (induced by source paper "recursive distributed detection for composite hypothesis testing nonlinear observation models in additive gaussian noise"):
  with members:
    - "distributed detection over adaptive networks refined asymptotics and the role of connectivity"
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "distributed inference over directed networks performance limits and optimal design"
    - "multitask diffusion adaptation over networks"
    - "distributed detection over noisy networks large deviations analysis"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "optimal joint target detection and parameter estimation by mimo radar"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-2 (induced by source paper "recursive distributed detection for composite hypothesis testing algorithms and asymptotics"):
  with members:
    - "multitask diffusion adaptation over networks"
    - "distributed detection over noisy networks large deviations analysis"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "optimal joint target detection and parameter estimation by mimo radar"
    - "gossip algorithms for distributed signal processing"

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)