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: "social learning over weakly connected graphs"
  Abstract: In this paper, we study diffusion social learning over weakly-connected graphs. We show that the asymmetric flow of information hinders the learning abilities of certain agents regardless of their local observations. Under some circumstances that we clarify in this work, a scenario of total influence (or "mind-control") arises where a set of influential agents ends up shaping the beliefs of non-influential agents. We derive useful closed-form expressions that characterize this influence, and which can be used to motivate design problems to control it. We provide simulation examples to illustrate the results.

Connected through hyperedge HE-1 (induced by source paper "learning graph influence from social interactions"):
  with members:
    - "local tomography of large networks under the low observability regime"
    - "consistent tomography under partial observations over adaptive networks"
    - "euclidean distance matrices essential theory algorithms and applications"
    - "information exchange and learning dynamics over weakly connected adaptive networks"

Connected through hyperedge HE-2 (induced by source paper "inverse graph learning over optimization networks"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "interplay between topology and social learning over weak graphs"
    - "local tomography of large networks under the low observability regime"
    - "consistent tomography under partial observations over adaptive networks"
    - "signal processing on graphs causal modeling of un structured data"
    - "network inference from consensus dynamics"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"

Connected through hyperedge HE-3 (induced by source paper "topology inference over networks with nonlinear coupling"):
  with members:
    - "consistent tomography under partial observations over adaptive networks"
    - "signal processing on graphs causal modeling of un structured data"
    - "linear identification of nonlinear systems a lifting technique based on the koopman operator"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "distributed detection over adaptive networks refined asymptotics and the role of connectivity"
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "efficiently learning ising models on arbitrary graphs"
    - "directed information graphs"

Connected through hyperedge HE-4 (induced by source paper "non bayesian social learning with uncertain models"):
  with members:
    - "defending non bayesian learning against adversarial attacks"
    - "exponentially fast parameter estimation in networks using distributed dual averaging"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-5 (induced by source paper "interplay between topology and social learning over weak graphs"):
  with members:
    - "local tomography of large networks under the low observability regime"
    - "consistent tomography under partial observations over adaptive networks"
    - "euclidean distance matrices essential theory algorithms and applications"
    - "information exchange and learning dynamics over weakly connected adaptive networks"
    - "interactive sensing and decision making 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)