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: "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
  Abstract: This paper considers gossip distributed estimation of a (static) distributed random field (a.k.a., large-scale unknown parameter vector) observed by sparsely interconnected sensors, each of which only observes a small fraction of the field. We consider linear distributed estimators whose structure combines the information flow among sensors (the consensus term resulting from the local gossiping exchange among sensors when they are able to communicate) and the information gathering measured by the sensors (the sensing or innovations term). This leads to mixed time scale algorithms-one time scale associated with the consensus and the other with the innovations. The paper establishes a distributed observability condition (global observability plus mean connectedness) under which the distributed estimates are consistent and asymptotically normal. We introduce the distributed notion equivalent to the (centralized) Fisher information rate, which is a bound on the mean square error reduction rate of any distributed estimator; we show that under the appropriate modeling and structural network communication conditions (gossip protocol) the distributed gossip estimator attains this distributed Fisher information rate, asymptotically achieving the performance of the optimal centralized estimator. Finally, we study the behavior of the distributed gossip estimator when the measurements fade (noise variance grows) with time; in particular, we consider the maximum rate at which the noise variance can grow and still the distributed estimator being consistent, by showing that, as long as the centralized estimator is consistent, the distributed estimator remains consistent.

Connected through hyperedge HE-1 (induced by source paper "supervised learning under distributed features"):
  with members:
    - "coordinate descent diffusion learning by networked agents"
    - "variance reduced stochastic learning by networked agents under random reshuffling"
    - "variance reduced stochastic learning under random reshuffling"
    - "distributed learning for stochastic generalized nash equilibrium problems"
    - "sdca without duality regularization and individual convexity"
    - "doubly random parallel stochastic methods for large scale learning"
    - "next in network nonconvex optimization"
    - "dictionary learning over distributed models"

Connected through hyperedge HE-2 (induced by source paper "topology inference over networks with nonlinear coupling"):
  with members:
    - "consistent tomography under partial observations over adaptive networks"
    - "signal processing on graphs causal modeling of un structured data"
    - "linear identification of nonlinear systems a lifting technique based on the koopman operator"
    - "social learning over weakly connected graphs"
    - "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"
    - "efficiently learning ising models on arbitrary graphs"

Connected through hyperedge HE-3 (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"
    - "exponentially fast parameter estimation in networks using distributed dual averaging"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-4 (induced by source paper "secure distributed filtering for unstable dynamics under compromised observations"):
  with members:
    - "secure state estimation with byzantine sensors a probabilistic approach"
    - "secure distributed state estimation of an lti system over time varying networks and analog erasure channels"
    - "detection and mitigation of biasing attacks on distributed estimation networks"
    - "convex optimization based state estimation against sparse integrity attacks"

Connected through hyperedge HE-5 (induced by source paper "distributed parameter estimation under event triggered communications"):
  with members:
    - "distributed kalman filters with state equality constraints time based and event triggered communications"
    - "stochastic event triggered sensor scheduling for remote state estimation"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-6 (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-7 (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-8 (induced by source paper "dynamic average diffusion with randomized coordinate updates"):
  with members:
    - "supervised learning under distributed features"
    - "stochastic gradient push for distributed deep learning"
    - "coordinate descent diffusion learning by networked agents"
    - "distributed big data optimization via block iterative convexification and averaging"
    - "next in network nonconvex optimization"
    - "dictionary learning over distributed models"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "distributed pareto optimization via diffusion strategies"

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)