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: "topology identification of directed dynamical networks via power spectral analysis"
  Abstract: We address the problem of identifying the topology of an unknown weighted, directed network of LTI systems stimulated by wide-sense stationary noises of unknown power spectral densities. We propose several reconstruction algorithms based on the cross-power spectral densities of the network's response to the input noises. Our first algorithm reconstructs the Boolean structure (i.e., existence and directions of links) of a directed network from a series of dynamical responses. Moreover, we propose a second algorithm to recover the exact structure of the network (including edge weights), as well as the power spectral density of the input noises, when an eigenvalue-eigenvector pair of the connectivity matrix is known (for example, Laplacian connectivity matrices). Finally, for the particular cases of nonreciprocal networks (i.e., networks with no directed edges pointing in opposite directions) and undirected networks, we propose specialized algorithms that result in a lower computational cost.

Connected through hyperedge HE-1 (induced by source paper "on inference of network topology and confirmation bias in cyber social networks"):
  with members:
    - "semi blind inference of topologies and dynamical processes over dynamic graphs"
    - "polarization and fake news early warning of potential misinformation targets"
    - "opinion dynamics with varying susceptibility to persuasion"
    - "spread of information with confirmation bias in cyber social networks"
    - "fake news in social networks"
    - "information theoretic approach to strategic communication as a hierarchical game"
    - "optimal investment strategies for competing camps in a social network a broad framework"
    - "network inference from consensus dynamics"

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"
    - "active sensing of social networks"

Connected through hyperedge HE-3 (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"
    - "active sensing of social networks"
    - "network structure inference a survey motivations methods and applications"

Connected through hyperedge HE-4 (induced by source paper "bayesian topology identification of linear dynamic networks"):
  with members:
    - "prediction error identification of linear dynamic networks with rank reduced noise"
    - "robust em kernel based methods for linear system identification"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-5 (induced by source paper "physics informed topology learning in networks of linear dynamical systems"):
  with members:
    - "data driven identification of a thermal network in multi zone building"
    - "learning the exact topology of undirected consensus networks"
    - "network inference from consensus dynamics"
    - "network topology inference from spectral templates"
    - "graph learning from data under laplacian and structural constraints"
    - "directed information graphs"
    - "consistency of the group lasso and multiple kernel learning"

Connected through hyperedge HE-6 (induced by source paper "network inference from consensus dynamics"):
  with members:
    - "network topology inference from spectral templates"
    - "network topology identification from spectral templates"
    - "network structure inference a survey motivations methods and applications"
    - "reconstruction of directed networks from consensus dynamics"

Connected through hyperedge HE-7 (induced by source paper "learning the exact topology of undirected consensus networks"):
  with members:
    - "exact topology reconstruction of radial dynamical systems with applications to distribution system of the power grid"
    - "optimal topology design for disturbance minimization in power grids"
    - "distributed constrained optimization by consensus based primal dual perturbation method"

Connected through hyperedge HE-8 (induced by source paper "identifying nonlinear 1 step causal influences in presence of latent variables"):
  with members:
    - "learning the dependence graph of time series with latent factors"

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)