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 detection over noisy networks large deviations analysis"
  Abstract: We study the large deviations performance of consensus+innovations distributed detection over noisy networks, where agents at a time step cooperate with their immediate neighbors (consensus) and assimilate their new observations (innovation.) We show that, under noisy communication, all agents can still achieve an exponential error rate, even when certain (or most) agents cannot detect the event of interest in isolation. The key to achieving this is the appropriate design of the time-varying weight sequence {αk = b0/(a + k)} by which agents weigh their neighbors' messages. We and a communication payoff threshold on the communication noise power, i.e., the critical noise power below which cooperation among neighbors improves detection performance and above which the noise in the communication among agents overwhelms the distributed detector performance. Numerical examples illustrate several tradeoffs among network parameters and between the time (or number of measurements) needed for a reliable decision and the transmission power invested by the agents.

Connected through hyperedge HE-1 (induced by source paper "distributed learning with adversarial agents under relaxed network condition"):
  with members:
    - "defending non bayesian learning against adversarial attacks"
    - "iterative byzantine vector consensus in incomplete graphs"
    - "exponentially fast parameter estimation in networks using distributed dual averaging"

Connected through hyperedge HE-2 (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"
    - "diffusion adaptation over networks"

Connected through hyperedge HE-3 (induced by source paper "distributed detection in ad hoc networks through quantized consensus"):
  with members:
    - "quantized consensus by the admm probabilistic versus deterministic quantizers"
    - "large deviations performance of consensus innovations distributed detection with non gaussian observations"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"
    - "topology for distributed inference on graphs"

Connected through hyperedge HE-4 (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"
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "large deviations performance of consensus innovations distributed detection with non gaussian observations"
    - "performance limits for distributed estimation over lms adaptive networks"

Connected through hyperedge HE-5 (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"
    - "consensus of multi agent networks in the presence of adversaries using only local information"
    - "large deviations performance of consensus innovations distributed detection with non gaussian observations"

Connected through hyperedge HE-6 (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"
    - "distributed bayesian quickest change detection in sensor networks via two layer large deviation analysis"
    - "multitask diffusion adaptation over networks"
    - "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-7 (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"
    - "large deviations performance of consensus innovations distributed detection with non gaussian observations"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-8 (induced by source paper "recursive distributed detection for composite hypothesis testing algorithms and asymptotics"):
  with members:
    - "distributed bayesian quickest change detection in sensor networks via two layer large deviation analysis"
    - "multitask diffusion adaptation over networks"
    - "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)