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 kalman filtering over massive data sets analysis through large deviations of random riccati equations"
  Abstract: This paper studies the convergence of the estimation error process and the characterization of the corresponding invariant measure in distributed Kalman filtering for potentially unstable and large linear dynamic systems. A gossip network protocol termed Modified Gossip Interactive Kalman Filtering (M-GIKF) is proposed, where sensors exchange their filtered states (estimates and error covariances) and propagate their observations via inter-sensor communications of rate $\overline{\gamma}$; $\overline{\gamma}$ is defined as the averaged number of inter-sensor message passages per signal evolution epoch. The filtered states are interpreted as stochastic particles swapped through local interaction. The paper shows that the conditional estimation error covariance sequence at each sensor under M-GIKF evolves as a random Riccati equation (RRE) with Markov modulated switching. By formulating the RRE as a random dynamical system, it is shown that the network achieves weak consensus, i.e., the conditional estimation error covariance at a randomly selected sensor converges weakly (in distribution) to a unique invariant measure. Further, it is proved that as $\overline{\gamma} \rightarrow \infty$ this invariant measure satisfies the Large Deviation (LD) upper and lower bounds, implying that this measure converges exponentially fast (in probability) to the Dirac measure $\delta_{P^*}$, where $P^*$ is the stable error covariance of the centralized (Kalman) filtering setup. The LD results answer a fundamental question on how to quantify the rate at which the distributed scheme approaches the centralized performance as the inter-sensor communication rate increases.

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"
    - "gossip and distributed kalman filtering weak consensus under weak detectability"
    - "greedy gossip with eavesdropping"

Connected through hyperedge HE-2 (induced by source paper "consensus innovations distributed kalman filter with optimized gains"):
  with members:
    - "on the genericity properties in distributed estimation topology design and sensor placement"
    - "gossip and distributed kalman filtering weak consensus under weak detectability"
    - "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-3 (induced by source paper "distributed estimation of dynamic fields over multi agent networks"):
  with members:
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-4 (induced by source paper "distributed inference over directed networks performance limits and optimal design"):
  with members:
    - "distributed detection and estimation in wireless sensor networks"
    - "asymptotic performance of adaptive distributed detection over networks"
    - "sparse distributed learning based on diffusion adaptation"
    - "large deviations performance of consensus innovations distributed detection with non gaussian observations"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-5 (induced by source paper "learning based distributed detection estimation in sensor networks with unknown sensor defects"):
  with members:
    - "switching to learn"
    - "distributed detection over noisy networks large deviations analysis"
    - "distributed recursive least squares stability and performance analysis"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "gossip algorithms for distributed signal processing"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"
    - "geographic gossip efficient averaging for sensor networks"

Connected through hyperedge HE-6 (induced by source paper "distributed bayesian quickest change detection in sensor networks via two layer large deviation analysis"):
  with members:
    - "distributed detection over noisy networks large deviations analysis"
    - "one shot schemes for decentralized quickest change detection"
    - "decentralized sequential change detection using physical layer fusion"

Connected through hyperedge HE-7 (induced by source paper "kalman filtering over fading channels zero one laws and almost sure stabilities"):
  with members:
    - "capacity per unit energy of fading channels with a peak constraint"

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)