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: "large deviations performance of consensus innovations distributed detection with non gaussian observations"
  Abstract: We establish the large deviations asymptotic performance (error exponent) of consensus+innovations distributed detection over random networks with generic (non-Gaussian) sensor observations. At each time instant, sensors 1) combine theirs with the decision variables of their neighbors (consensus) and 2) assimilate their new observations (innovations). This paper shows for general non-Gaussian distributions that consensus+innovations distributed detection exhibits a phase transition behavior with respect to the network degree of connectivity. Above a threshold, distributed is as good as centralized, with the same optimal asymptotic detection performance, but, below the threshold, distributed detection is suboptimal with respect to centralized detection. We determine this threshold and quantify the performance loss below threshold. Finally, we show the dependence of the threshold and of the performance on the distribution of the observations: the asymptotic performance of distributed detectors over the same random network with different observations' distributions, for example, Gaussian, Laplace, or quantized, may be different, even though the asymptotic performance of the corresponding centralized detectors is the same.

Connected through hyperedge HE-1 (induced by source paper "detection under one bit messaging over adaptive networks"):
  with members:
    - "quantized consensus by the admm probabilistic versus deterministic quantizers"
    - "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 parameter estimation with quantized communication via running average"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "active sequential hypothesis testing"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "distributed detection over noisy networks large deviations analysis"

Connected through hyperedge HE-2 (induced by source paper "local tomography of large networks under the low observability regime"):
  with members:
    - "consistent tomography under partial observations over adaptive networks"
    - "signal processing on graphs causal modeling of un structured data"
    - "network inference from consensus dynamics"
    - "linear identification of nonlinear systems a lifting technique based on the koopman operator"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "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"
    - "on the learning behavior of adaptive networks part ii performance analysis"

Connected through hyperedge HE-3 (induced by source paper "fade fast and asymptotically efficient distributed estimator for dynamic networks"):
  with members:
    - "diffusion adaptation over networks"
    - "fast distributed gradient methods"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-4 (induced by source paper "distributed detection in ad hoc networks through quantized consensus"):
  with members:
    - "quantized consensus by the admm probabilistic versus deterministic quantizers"
    - "distributed detection over noisy networks large deviations analysis"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"
    - "topology for distributed inference on graphs"

Connected through hyperedge HE-5 (induced by source paper "consistent tomography under partial observations over adaptive networks"):
  with members:
    - "signal processing on graphs causal modeling of un structured data"
    - "graph reconstruction from the observation of diffused signals"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "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"
    - "signals on graphs uncertainty principle and sampling"
    - "information diffusion of topic propagation in social media"

Connected through hyperedge HE-6 (induced by source paper "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"):
  with members:
    - "exponentially fast parameter estimation in networks using distributed dual averaging"
    - "distributed pareto optimization via diffusion strategies"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "distributed detection over noisy networks large deviations analysis"
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "performance limits for distributed estimation over lms adaptive networks"

Connected through hyperedge HE-7 (induced by source paper "defending non bayesian learning against adversarial attacks"):
  with members:
    - "learning without recall a case for log linear learning"
    - "finite time analysis of the distributed detection problem"
    - "reaching approximate byzantine consensus with multi hop communication"
    - "byzantine vector consensus in complete graphs"
    - "iterative byzantine vector consensus in incomplete graphs"
    - "exponentially fast parameter estimation in networks using distributed dual averaging"
    - "distributed detection over noisy networks large deviations analysis"
    - "consensus of multi agent networks in the presence of adversaries using only local information"

Connected through hyperedge HE-8 (induced by source paper "distributed detection over adaptive networks refined asymptotics and the role of connectivity"):
  with members:
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "distributed detection and estimation in wireless sensor networks"
    - "asymptotic performance of adaptive distributed detection over networks"
    - "distributed detection over noisy networks large deviations analysis"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "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)