human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "consensus innovations distributed kalman filter with optimized gains"):
  Abstract: In this paper, we address the distributed filtering and prediction of time-varying random fields represented by linear time-invariant (LTI) dynamical systems. The field is observed by a sparsely connected network of agents/sensors collaborating among themselves. We develop a Kalman filter type  consensus  +  innovations   distributed linear estimator of the dynamic field termed as  Consensus+Innovations  Kalman Filter. We analyze the convergence properties of this distributed estimator. We prove that the mean-squared error of the estimator asymptotically converges if the degree of instability of the field dynamics is within a prespecified threshold defined as tracking capacity of the estimator. The tracking capacity is a function of the local observation models and the agent communication network. We design the optimal consensus and innovation gain matrices yielding distributed estimates with minimized mean-squared error. Through numerical evaluations, we show that the distributed estimator with optimal gains converges faster and with approximately 3dB better mean-squared error performance than previous distributed estimators.

Includes members:
  Member "distributed kalman filtering over massive data sets analysis through large deviations of random riccati equations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sample greedy gossip for distributed network wide average computation")
  Member "on the genericity properties in distributed estimation topology design and sensor placement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the controllability of clustered scale free networks")
  Member "gossip and distributed kalman filtering weak consensus under weak detectability":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sample greedy gossip for distributed network wide average computation")
  Member "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "supervised learning under distributed features")
  Member "networked estimation under information constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed pareto optimization via diffusion strategies")
  Member "gossip algorithms for distributed signal processing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "inverse graph learning over optimization networks")
  Member "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed learning in the non convex world from batch to streaming data and beyond")
  Member "distributing the kalman filter for large scale systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "coded distributed tracking")

Question: Please tell me which class the source paper 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)