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: "found graph data and planted vertex covers"
  Abstract: A typical way in which network data is recorded is to measure all the interactions among a specified set of core nodes; this produces a graph containing this core together with a potentially larger set of fringe nodes that have links to the core. Interactions between pairs of nodes in the fringe, however, are not recorded by this process, and hence not present in the resulting graph data. For example, a phone service provider may only have records of calls in which at least one of the participants is a customer; this can include calls between a customer and a non-customer, but not between pairs of non-customers. #R##N#Knowledge of which nodes belong to the core is an important piece of metadata that is crucial for interpreting the network dataset. But in many cases, this metadata is not available, either because it has been lost due to difficulties in data provenance, or because the network consists of found data obtained in settings such as counter-surveillance. This leads to a natural algorithmic problem, namely the recovery of the core set. Since the core set forms a vertex cover of the graph, we essentially have a planted vertex cover problem, but with an arbitrary underlying graph. We develop a theoretical framework for analyzing this planted vertex cover problem, based on results in the theory of fixed-parameter tractability, together with algorithms for recovering the core. Our algorithms are fast, simple to implement, and out-perform several methods based on network core-periphery structure on various real-world datasets.

Connected through hyperedge HE-1 (induced by source paper "planted hitting set recovery in hypergraphs"):
  with members:
    - "three hypergraph eigenvector centralities"
    - "core fringe link prediction"
    - "simplicial closure and higher order link prediction"
    - "bubing massive crawling for the masses"
    - "social networks under stress"
    - "pagerank beyond the web"
    - "axioms for centrality"

Connected through hyperedge HE-2 (induced by source paper "link prediction in networks with core fringe data"):
  with members:
    - "snorkel rapid training data creation with weak supervision"
    - "social networks under stress"
    - "identification of core periphery structure in networks"
    - "exact recovery in the stochastic block model"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-3 (induced by source paper "random spatial network models with core periphery structure"):
  with members:
    - "higher order clustering in networks"
    - "a nonlinear spectral method for core periphery detection in networks"
    - "coin flipping ball dropping and grass hopping for generating random graphs from matrices of edge probabilities"
    - "classifying developers into core and peripheral an empirical study on count and network metrics"
    - "detection of core periphery structure in networks using spectral methods and geodesic paths"
    - "identification of core periphery structure in networks"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-4 (induced by source paper "core fringe link prediction"):
  with members:
    - "snorkel rapid training data creation with weak supervision"
    - "social networks under stress"
    - "exact recovery in the stochastic block model"
    - "supervised random walks predicting and recommending links 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)