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 and recursive parameter estimation in parametrized linear state space models"
  Abstract: We consider a network of sensors deployed to sense a spatio-temporal field and estimate a parameter of interest. We are interested in the case where the temporal process sensed by each sensor can be modeled as a state-space process that is perturbed by random noise and parametrized by an unknown parameter. To estimate the unknown parameter from the measurements that the sensors sequentially collect, we propose a distributed and recursive estimation algorithm, which we refer to as the incremental recursive prediction error algorithm. This algorithm has the distributed property of incremental gradient algorithms and the on-line property of recursive prediction error algorithms. We study the convergence behavior of the algorithm and provide sufficient conditions for its convergence. Our convergence result is rather general and contains as special cases the known convergence results for the incremental versions of the least-mean square algorithm. Finally, we use the algorithm developed in this paper to identify the source of a gas-leak (diffusing source) in a closed warehouse and also report numerical simulations to verify convergence.

Connected through hyperedge HE-1 (induced by source paper "explicit convergence rate of a distributed alternating direction method of multipliers"):
  with members:
    - "linear convergence rate of a class of distributed augmented lagrangian algorithms"
    - "asynchronous distributed optimization using a randomized alternating direction method of multipliers"
    - "a reliable effective terascale linear learning system"

Connected through hyperedge HE-2 (induced by source paper "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"):
  with members:
    - "gossip and distributed kalman filtering weak consensus under weak detectability"
    - "gossip algorithms for distributed signal processing"
    - "weight optimization for consensus algorithms with correlated switching topology"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"
    - "sensor networks with random links topology design for distributed consensus"
    - "distributing the kalman filter for large scale systems"
    - "topology for distributed inference on graphs"

Connected through hyperedge HE-3 (induced by source paper "networked estimation under information constraints"):
  with members:
    - "distributed sensor localization in random environments using minimal number of anchor nodes"
    - "distributed consensus algorithms in sensor networks link failures and channel noise"

Connected through hyperedge HE-4 (induced by source paper "gossip algorithms for distributed signal processing"):
  with members:
    - "location aided fast distributed consensus in wireless networks"
    - "weight optimization for consensus algorithms with correlated switching topology"
    - "optimization and analysis of distributed averaging with short node memory"
    - "distributed lossy averaging"
    - "polynomial filtering for fast convergence in distributed consensus"
    - "distributed sensor localization in random environments using minimal number of anchor nodes"
    - "the impact of mobility on gossip algorithms"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-5 (induced by source paper "greedy gossip with eavesdropping"):
  with members:
    - "geographic gossip efficient aggregation for sensor networks"

Connected through hyperedge HE-6 (induced by source paper "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"):
  with members:
    - "distributed detection over noisy networks large deviations analysis"
    - "gossip and distributed kalman filtering weak consensus under weak detectability"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "weight optimization for consensus algorithms with correlated switching topology"
    - "sensor networks with random links topology design for distributed consensus"
    - "distributing the kalman filter for large scale systems"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"
    - "topology for distributed inference on graphs"

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.DC(Distributed, Parallel, and Cluster Computing)