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: "on the genericity properties in distributed estimation topology design and sensor placement"
  Abstract: In this paper, we consider distributed estimation of linear, discrete-time dynamical systems monitored by a network of agents. We require the agents to exchange information with their neighbors only once per dynamical system time-scale and study the network topology sufficient for distributed observability. To this aim, we provide a novel measurement-based agent classification: Type-   $\alpha,\beta$  , and    $\gamma$  , which leads to the construction of specific graph topologies:   ${\cal G}_\alpha$   and   ${\cal G}_\beta$  . In particular, in    ${\cal G}_\alpha$  , every Type-  $\alpha$   agent has a direct connection to every other agent, whereas, in    ${\cal G}_\beta$  , every agent has a directed path to every Type-  $\beta$    agent. With the help of these constructs, we formulate an estimator where measurement and predictor-fusion are implemented over    ${\cal G}_\alpha$   and    ${\cal G}_\beta$  , respectively, and show that the proposed scheme leads to distributed observability, i.e., observability of the distributed estimator. In order to characterize the estimator further, we show that Type-  $\alpha$    agents only exist in systems with    $S$  -rank (maximal rank of zero/non-zero pattern) deficient system matrices. In other words, systems with full   $S$  -rank matrices only have Type-  $\beta$    agents, and thus, a strongly-connected (agent) network is sufficient for full    $S$  -rank systems—by the definition of   ${\cal G}_\beta$    above; however strong-connectivity is not necessary, i.e., there exist weakly-connected networks that result in distributed observability. Furthermore, we show that for   $S$   -rank deficient systems, measurement-fusion over    ${\cal G}_\alpha$   is required, and predictor-fusion alone is insufficient. The approach taken in this paper is structural, i.e., we use the concept of structured systems theory and generic observability to derive the results. Finally, we provide an iterative method to compute the local estimator gain at each agent once the observability is ensured using the aforementioned construction.

Connected through hyperedge HE-1 (induced by source paper "on the controllability of clustered scale free networks"):
  with members:
    - "cyber social systems modeling inference and optimal design"
    - "distributed estimation recovery under sensor failure"
    - "controllability of social networks and the strategic use of random information"
    - "observational equivalence in system estimation contractions in complex networks"
    - "control principles of complex networks"
    - "measurement partitioning and observational equivalence in state estimation"
    - "graph theoretic distributed inference in social networks"
    - "multi agent consensus with relative state dependent measurement noises"

Connected through hyperedge HE-2 (induced by source paper "cyber social systems modeling inference and optimal design"):
  with members:
    - "structural cost optimal design of sensor networks for distributed estimation"
    - "distributed estimation recovery under sensor failure"
    - "observational equivalence in system estimation contractions in complex networks"
    - "active sensing of social networks"
    - "measurement partitioning and observational equivalence in state estimation"
    - "graph theoretic distributed inference in social networks"
    - "gossip algorithms for distributed signal processing"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-3 (induced by source paper "detection and mitigation of biasing attacks on distributed estimation networks"):
  with members:
    - "byzantine resilient distributed observers for lti systems"
    - "controller jammer game models of denial of service in control systems operating over packet dropping links"
    - "detection of biasing attacks on distributed estimation networks"
    - "distributed observers for lti systems"
    - "gain scheduled synchronization of parameter varying systems via relative h consensus with application to synchronization of uncertain bilinear systems"
    - "detectability of distributed consensus based observer networks an elementary analysis and extensions"
    - "conditions for detectability in distributed consensus based observer networks"

Connected through hyperedge HE-4 (induced by source paper "structural cost optimal design of sensor networks for distributed estimation"):
  with members:
    - "distributed estimation recovery under sensor failure"
    - "measurement partitioning and observational equivalence in state estimation"
    - "graph theoretic distributed inference in social networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"

Connected through hyperedge HE-5 (induced by source paper "consensus innovations distributed kalman filter with optimized gains"):
  with members:
    - "distributed kalman filtering over massive data sets analysis through large deviations of random riccati equations"
    - "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-6 (induced by source paper "distributed estimation recovery under sensor failure"):
  with members:
    - "consensus innovations distributed kalman filter with optimized gains"
    - "graph theoretic distributed inference in social networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"

Connected through hyperedge HE-7 (induced by source paper "graph theoretic distributed inference in social networks"):
  with members:
    - "a structured systems approach for optimal actuator sensor placement in linear time invariant systems"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "opinion dynamics in social networks a local interaction game with stubborn agents"
    - "conditions for detectability in distributed consensus based observer networks"
    - "using a model of social dynamics to predict popularity of news"
    - "distributing the kalman filter for large scale systems"

Connected through hyperedge HE-8 (induced by source paper "distributed kalman filtering over massive data sets analysis through large deviations of random riccati equations"):
  with members:
    - "gossip and distributed kalman filtering weak consensus under weak detectability"
    - "gossip algorithms for distributed signal processing"
    - "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.MA(Multiagent Systems)