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: "a distributed observer for a time invariant linear system"
  Abstract: A time-invariant, linear, distributed observer is described for estimating the state of an    $m>0$   channel,   $n$   -dimensional continuous-time linear system of the form    $\dot{x} = Ax,\;y_i = C_ix,\;i\in \lbrace 1,2,\ldots, m\rbrace$  . The state   $x$   is simultaneously estimated by   $m$   agents assuming each agent    $i$   senses   $y_i$    and receives the state   $z_j$    of each of its neighbors’ estimators. Neighbor relations are characterized by a constant directed graph   $\mathbb {N}$   whose vertices correspond to agents and whose arcs depict neighbor relations. For the case when the neighbor graph is strongly connected, the overall distributed observer consists of   $m$    linear estimators, one for each agent;   $m-1$    of the estimators are of dimension   $n$    and one estimator is of dimension   $n+m-1$   . Using results from the classical decentralized control theory, it is shown that subject to the assumptions that none of the   $C_i$   are zero, the neighbor graph   $\mathbb {N}$   is strongly connected, the system whose state to be estimated is jointly observable, and nothing more, it is possible to freely assign the spectrum of the overall distributed observer. For the more general case, when    $\mathbb {N}$   has    $q>1$   strongly connected components, it is explained how to construct a family of   $q$   distributed observers, one for each component, which can estimate   $x$   at a preassigned convergence rate.

Connected through hyperedge HE-1 (induced by source paper "a distributed observer for a discrete time linear system"):
  with members:
    - "a hybrid observer for a distributed linear system with a changing neighbor graph"
    - "distributed observers for lti systems"
    - "a distributed algorithm for solving a linear algebraic equation"

Connected through hyperedge HE-2 (induced by source paper "byzantine resilient distributed observers for lti systems"):
  with members:
    - "secure distributed state estimation of an lti system over time varying networks and analog erasure channels"
    - "detection and mitigation of biasing attacks on distributed estimation networks"
    - "detection of biasing attacks on distributed estimation networks"
    - "distributed observers for lti systems"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"
    - "distributing the kalman filter for large scale systems"

Connected through hyperedge HE-3 (induced by source paper "finite time distributed state estimation over time varying graphs exploiting the age of information"):
  with members:
    - "byzantine resilient distributed observers for lti systems"
    - "a hybrid observer for a distributed linear system with a changing neighbor graph"
    - "distributed observers for lti systems"
    - "a distributed algorithm for solving a linear algebraic equation"
    - "conditions for detectability in distributed consensus based observer networks"

Connected through hyperedge HE-4 (induced by source paper "secure distributed state estimation of an lti system over time varying networks and analog erasure channels"):
  with members:
    - "resilient consensus of second order agent networks asynchronous update rules with delays"
    - "detection of biasing attacks on distributed estimation networks"
    - "distributed observers for lti systems"
    - "byzantine multi agent optimization part ii"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"

Connected through hyperedge HE-5 (induced by source paper "distributed observers for lti systems"):
  with members:
    - "detectability of distributed consensus based observer networks an elementary analysis and extensions"
    - "distributed robust estimation over randomly switching networks using h consensus"
    - "conditions for detectability in distributed consensus based observer networks"
    - "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.SY(Systems and Control)