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: "active sensing of social networks"
  Abstract: This paper develops an active sensing method to estimate the relative weight (or trust) agents place on their neighbors’ information in a social network. The model used for the regression is based on the steady state equation in the linear DeGroot model under the influence of stubborn agents; i.e., agents whose opinions are not influenced by their neighbors. This method can be viewed as a  social RADAR , where the stubborn agents excite the system and the latter can be estimated through the reverberation observed from the analysis of the agents’ opinions. The social network sensing problem can be interpreted as a blind compressed sensing problem with a sparse measurement matrix. We prove that the network structure will be revealed when a sufficient number of stubborn agents independently influence a number of ordinary (non-stubborn) agents. We investigate the scenario with a deterministic or randomized DeGroot model and propose a consistent estimator of the steady states for the latter scenario. Simulation results on synthetic and real world networks support our findings.

Connected through hyperedge HE-1 (induced by source paper "exact blind community detection from signals on multiple graphs"):
  with members:
    - "network inference from consensus dynamics with unknown parameters"
    - "blind inference of centrality rankings from graph signals"
    - "blind identification of stochastic block models from dynamical observations"
    - "blind community detection from low rank excitations of a graph filter"
    - "blind identification of graph filters"
    - "network inference from consensus dynamics"
    - "network topology inference from spectral templates"
    - "graph learning from data under laplacian and structural constraints"

Connected through hyperedge HE-2 (induced by source paper "blind identification of stochastic block models from dynamical observations"):
  with members:
    - "spectral partitioning of time varying networks with unobserved edges"
    - "blind community detection from low rank excitations of a graph filter"
    - "the sample complexity of multi reference alignment"
    - "centrality measures for graphons accounting for uncertainty in networks"
    - "network inference from consensus dynamics"
    - "the ground truth about metadata and community detection in networks"
    - "network topology inference from spectral templates"
    - "reconstruction of directed networks from consensus dynamics"

Connected through hyperedge HE-3 (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"
    - "measurement partitioning and observational equivalence in state estimation"
    - "graph theoretic distributed inference in social networks"
    - "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-4 (induced by source paper "spectral partitioning of time varying networks with unobserved edges"):
  with members:
    - "blind identification of stochastic block models from dynamical observations"
    - "blind community detection from low rank excitations of a graph filter"
    - "the many facets of community detection in complex networks"
    - "network topology inference from spectral templates"
    - "network structure inference a survey motivations methods and applications"
    - "topology identification of directed dynamical networks via power spectral analysis"

Connected through hyperedge HE-5 (induced by source paper "opinion dynamics with random actions and a stubborn agent"):
  with members:
    - "opinion dynamics with stubborn agents"
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "the impact of random actions on opinion dynamics"

Connected through hyperedge HE-6 (induced by source paper "blind community detection from low rank excitations of a graph filter"):
  with members:
    - "latent agents in networks estimation and pricing"
    - "graph learning from filtered signals graph system and diffusion kernel identification"
    - "blind identification of graph filters"
    - "autoregressive moving average graph filtering"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "network topology inference from spectral templates"
    - "learning sparse graphs under smoothness prior"
    - "compressive spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "learning quadratic games on networks"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "learning graphical games from behavioral data sufficient and necessary conditions"
    - "targeting interventions in networks"
    - "learning sparse potential games in polynomial time and sample complexity"
    - "learning laplacian matrix in smooth graph signal representations"
    - "graphical models for game theory"
    - "learning the structure and parameters of large population graphical games from behavioral data"
    - "uncovering the temporal dynamics of diffusion networks"

Connected through hyperedge HE-8 (induced by source paper "randomized opinion dynamics over networks influence estimation from partial observations"):
  with members:
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "novel multidimensional models of opinion dynamics in social networks"
    - "ergodic randomized algorithms and dynamics over networks"
    - "opinion fluctuations and disagreement in social 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.SI(Social and Information Networks)