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 robust estimation over randomly switching networks using h consensus"
  Abstract: The paper considers a distributed robust estimation problem over a network with Markovian randomly varying topology. The objective is to deal with network variations locally, by switching observer gains at affected nodes only. We propose sufficient conditions which guarantee a suboptimal H"~ level of relative disagreement of estimates in such observer networks. When the status of the network is known globally, these sufficient conditions enable the network gains to be computed by solving certain LMIs. When the nodes are to rely on a locally available information about the network topology, additional rank constraints are used to condition the gains, given this information. The results are complemented by necessary conditions which relate properties of the interconnection graph Laplacian to the mean-square detectability of the plant through measurement and interconnection channels.

Connected through hyperedge HE-1 (induced by source paper "robust detection of biasing attacks on misappropriated distributed observers via decentralized h_ infty synthesis"):
  with members:
    - "detection of biasing attacks on distributed estimation networks"
    - "gain scheduled synchronization of parameter varying systems via relative h consensus with application to synchronization of uncertain bilinear systems"
    - "minimum energy distributed filtering"

Connected through hyperedge HE-2 (induced by source paper "distributed kalman filter in a network of linear dynamical systems"):
  with members:
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-3 (induced by source paper "distributed observers for lti systems"):
  with members:
    - "a distributed observer for a time invariant linear system"
    - "detectability of distributed consensus based observer networks an elementary analysis and extensions"
    - "conditions for detectability in distributed consensus based observer networks"
    - "distributing the kalman filter for large scale systems"

Connected through hyperedge HE-4 (induced by source paper "cooperative robust estimation with local performance guarantees"):
  with members:
    - "gain scheduled synchronization of parameter varying systems via relative h consensus with application to synchronization of uncertain bilinear systems"
    - "minimum energy distributed filtering"

Connected through hyperedge HE-5 (induced by source paper "cooperative ℋ estimation for large scale interconnected linear systems"):
  with members:
    - "cooperative estimation for synchronization of heterogeneous multi agent systems using relative information"

Connected through hyperedge HE-6 (induced by source paper "minimum energy distributed filtering"):
  with members:
    - "conditions for detectability in distributed consensus based observer networks"

Connected through hyperedge HE-7 (induced by source paper "detectability of distributed consensus based observer networks an elementary analysis and extensions"):
  with members:
    - "conditions for detectability in distributed consensus based observer networks"

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.SY(Systems and Control)