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: "exponentially fast parameter estimation in networks using distributed dual averaging"
  Abstract: In this paper we present an optimization-based view of distributed parameter estimation and observational social learning in networks. Agents receive a sequence of random, independent and identically distributed (i.i.d.) signals, each of which individually may not be informative about the underlying true state, but the signals together are globally informative enough to make the true state identifiable. Using an optimization-based characterization of Bayesian learning as proximal stochastic gradient descent (with Kullback-Leibler divergence from a prior as a proximal function), we show how to efficiently use a distributed, online variant of Nesterov's dual averaging method to solve the estimation with purely local information. When the true state is globally identifiable, and the network is connected, we prove that agents eventually learn the true parameter using a randomized gossip scheme. We demonstrate that with high probability the convergence is exponentially fast with a rate dependent on the KL divergence of observations under the true state from observations under the second likeliest state. Furthermore, our work also highlights the possibility of learning under continuous adaptation of network which is a consequence of employing constant, unit stepsize for the algorithm.

Connected through hyperedge HE-1 (induced by source paper "a new approach for distributed hypothesis testing with extensions to byzantine resilience"):
  with members:
    - "byzantine resilient distributed observers for lti systems"
    - "distributed observers for lti systems"
    - "defending non bayesian learning against adversarial attacks"
    - "distributed optimization under adversarial nodes"
    - "matrix representation of iterative approximate byzantine consensus in directed graphs"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"

Connected through hyperedge HE-2 (induced by source paper "non bayesian social learning with uncertain models"):
  with members:
    - "social learning over weakly connected graphs"
    - "defending non bayesian learning against adversarial attacks"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-3 (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"
    - "distributed detection over noisy networks large deviations analysis"

Connected through hyperedge HE-4 (induced by source paper "belief control strategies for interactions over weak graphs"):
  with members:
    - "active sensing of social networks"
    - "information exchange and learning dynamics over weakly connected adaptive networks"
    - "information diffusion in social sensing"
    - "higher dimensional consensus learning in large scale networks"

Connected through hyperedge HE-5 (induced by source paper "distributed learning from interactions in social networks"):
  with members:
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "next in network nonconvex optimization"
    - "a distributed classification estimation algorithm for sensor networks"
    - "decentralized maximum likelihood estimation for sensor networks composed of nonlinearly coupled dynamical systems"

Connected through hyperedge HE-6 (induced by source paper "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"):
  with members:
    - "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"
    - "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-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"
    - "distributed detection over noisy networks large deviations analysis"
    - "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-8 (induced by source paper "bayesian heuristics for group decisions"):
  with members:
    - "learning without recall a case for log linear learning"
    - "learning without recall by random walks on directed graphs"
    - "groupthink and the failure of information aggregation in large groups"

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.LG(Machine Learning)