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: "graph theoretic distributed inference in social networks"
  Abstract: We consider distributed inference in social networks where a phenomenon of interest evolves over a given social interaction graph, referred to as the social digraph. We assume that a network of agents monitors certain nodes in the social digraph and the agents rely on inter-agent communication to perform inference. The key contributions include: (i) a novel construction of the distributed estimator and distributed observability from the first principles; (ii) a graph-theoretic agent classification that establishes the importance and role of each agent towards inference; (iii) characterizing the necessary conditions, based on the classification in (ii), on the agent network to achieve distributed observability. Our results are based on structured systems theory and are applicable to any parameter choice of the underlying system matrix as long as the social digraph remains fixed. In other words, any social phenomena that evolves (linearly) over a structure-invariant social digraph may be considered-we refer to such systems as Liner Structure-Invariant (LSI). The aforementioned contributions, (i)-(iii), thus, only require the knowledge of the social digraph (topology) and are independent of the social phenomena. We show the applicability of the results to several real-wold social networks, i.e. social influence among monks, networks of political blogs and books, and a co-authorship graph.

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"
    - "on the genericity properties in distributed estimation topology design and sensor placement"
    - "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"
    - "on the genericity properties in distributed estimation topology design and sensor placement"
    - "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 "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"
    - "on the genericity properties in distributed estimation topology design and sensor placement"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"

Connected through hyperedge HE-4 (induced by source paper "distributed estimation recovery under sensor failure"):
  with members:
    - "consensus innovations distributed kalman filter with optimized gains"
    - "on the genericity properties in distributed estimation topology design and sensor placement"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"

Connected through hyperedge HE-5 (induced by source paper "observational equivalence in system estimation contractions in complex networks"):
  with members:
    - "sensor selection cost optimisation for tracking structurally cyclic systems a p order solution"
    - "local rewiring algorithms to increase clustering and grow a small world"
    - "control principles of complex networks"

Connected through hyperedge HE-6 (induced by source paper "sensor selection cost optimisation for tracking structurally cyclic systems a p order solution"):
  with members:
    - "minimal actuator placement with optimal control constraints"
    - "a supermodular optimization framework for leader selection under link noise in linear multi agent systems"

Connected through hyperedge HE-7 (induced by source paper "active sensing of social networks"):
  with members:
    - "signal processing on graphs modeling causal relations in big data"
    - "the social system identification problem"
    - "analysis and control of beliefs in social networks"
    - "ergodic randomized algorithms and dynamics over networks"
    - "opinion fluctuations and disagreement in social networks"
    - "social structure of facebook networks"
    - "a unique nonnegative solution to an underdetermined system from vectors to matrices"
    - "higher dimensional consensus learning in large scale networks"

Connected through hyperedge HE-8 (induced by source paper "the social system identification problem"):
  with members:
    - "active sensing of social networks"
    - "efficiently learning ising models on arbitrary graphs"
    - "analysis and control of beliefs in social networks"
    - "ergodic randomized algorithms and dynamics over networks"
    - "opinion fluctuations and disagreement in social networks"
    - "modified cs modifying compressive sensing for problems with partially known support"

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.SI(Social and Information Networks)