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: "gossip and distributed kalman filtering weak consensus under weak detectability"
  Abstract: The paper presents the gossip interactive Kalman filter (GIKF) for distributed Kalman filtering for networked systems and sensor networks, where intersensor communication and observations occur at the same time-scale. The communication among sensors is random; each sensor occasionally exchanges its filtering state information with a neighbor depending on the availability of the appropriate network link. We show that under a weak distributed detectability condition: 1) the GIKF error process remains stochastically bounded, irrespective of the instability of the random process dynamics; and 2) the network achieves weak consensus, i.e., the conditional estimation error covariance at a (uniformly) randomly selected sensor converges in distribution to a unique invariant measure on the space of positive semidefinite matrices (independent of the initial state). To prove these results, we interpret the filtering states (estimates and error covariances) at each node in the GIKF as stochastic particles with local interactions. We analyze the asymptotic properties of the error process by studying as a random dynamical system the associated switched (random) Riccati equation, the switching being dictated by a nonstationary Markov chain on the network graph.

Connected through hyperedge HE-1 (induced by source paper "sample greedy gossip for distributed network wide average computation"):
  with members:
    - "randomized consensus based distributed kalman filtering over wireless sensor networks"
    - "distributed kalman filtering over massive data sets analysis through large deviations of random riccati equations"
    - "greedy gossip with eavesdropping"

Connected through hyperedge HE-2 (induced by source paper "compressive privacy for a linear dynamical system"):
  with members:
    - "plausible deniability for privacy preserving data synthesis"
    - "hypothesis testing under maximal leakage privacy constraints"
    - "information theoretic attacks in the smart grid"
    - "privacy aware mmse estimation"
    - "inferential privacy guarantees for differentially private mechanisms"
    - "differential privacy as a mutual information constraint"
    - "distributed local linear parameter estimation using gaussian spawn"
    - "robust decentralized detection and social learning in tandem networks"

Connected through hyperedge HE-3 (induced by source paper "consensus innovations distributed kalman filter with optimized gains"):
  with members:
    - "distributed kalman filtering over massive data sets analysis through large deviations of random riccati equations"
    - "on the genericity properties in distributed estimation topology design and sensor placement"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "networked estimation under information constraints"
    - "gossip algorithms for distributed signal processing"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"
    - "distributing the kalman filter for large scale systems"

Connected through hyperedge HE-4 (induced by source paper "distributed kalman filters with state equality constraints time based and event triggered communications"):
  with members:
    - "stochastic event triggered sensor scheduling for remote state estimation"

Connected through hyperedge HE-5 (induced by source paper "optimizing the gossip algorithm with non uniform clock distribution over classical quantum networks"):
  with members:
    - "finite time convergent gossiping"
    - "optimizing the convergence rate of the continuous time quantum consensus"
    - "optimizing the convergence rate of the quantum consensus a discrete time model"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"
    - "the impact of mobility on gossip algorithms"
    - "geographic gossip efficient averaging for sensor networks"
    - "topology for distributed inference on graphs"

Connected through hyperedge HE-6 (induced by source paper "distributed estimation of a parametric field algorithms and performance analysis"):
  with members:
    - "distributed estimation of a parametric field using sparse noisy data"
    - "distributed particle filter implementation with intermittent irregular consensus convergence"
    - "one bit distributed sensing and coding for field estimation in sensor networks"
    - "estimation diversity and energy efficiency in distributed sensing"

Connected through hyperedge HE-7 (induced by source paper "distributed kalman filtering over massive data sets analysis through large deviations of random riccati equations"):
  with members:
    - "on the genericity properties in distributed estimation topology design and sensor placement"
    - "gossip algorithms for distributed signal processing"
    - "distributing the kalman filter for large scale systems"

Connected through hyperedge HE-8 (induced by source paper "distributed widely linear complex kalman filtering"):
  with members:
    - "distributing the kalman filter for large scale systems"

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)