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: "locally estimating core numbers"
  Abstract: Graphs are a powerful way to model interactions and relationships in data from a wide variety of application domains. In this setting, entities represented by vertices at the "center" of the graph are often more important than those associated with vertices on the "fringes". For example, central nodes tend to be more critical in the spread of information or disease and play an important role in clustering/community formation. Identifying such "core" vertices has recently received additional attention in the context of {\em network experiments}, which analyze the response when a random subset of vertices are exposed to a treatment (e.g. inoculation, free product samples, etc). Specifically, the likelihood of having many central vertices in any exposure subset can have a significant impact on the experiment. #R##N#We focus on using $k$-cores and core numbers to measure the extent to which a vertex is central in a graph. Existing algorithms for computing the core number of a vertex require the entire graph as input, an unrealistic scenario in many real world applications. Moreover, in the context of network experiments, the subgraph induced by the treated vertices is only known in a probabilistic sense. We introduce a new method for estimating the core number based only on the properties of the graph within a region of radius $\delta$ around the vertex, and prove an asymptotic error bound of our estimator on random graphs. Further, we empirically validate the accuracy of our estimator for small values of $\delta$ on a representative corpus of real data sets. Finally, we evaluate the impact of improved local estimation on an open problem in network experimentation posed by Ugander et al.

Connected through hyperedge HE-1 (induced by source paper "local algorithms for hierarchical dense subgraph discovery"):
  with members:
    - "shared memory graph truss decomposition"
    - "estimation of graphlet statistics"
    - "static graph challenge subgraph isomorphism"
    - "higher order organization of complex networks"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "parallel peeling algorithms"
    - "graph cluster randomization network exposure to multiple universes"
    - "truss decomposition in massive networks"

Connected through hyperedge HE-2 (induced by source paper "using core periphery structure to predict high centrality nodes in time varying networks"):
  with members:
    - "time series analysis of temporal networks"
    - "understanding stability of noisy networks through centrality measures and local connections"
    - "centrality metrics and localization in core periphery networks"
    - "predicting node degree centrality with the node prominence profile"
    - "centrality metric for dynamic networks"

Connected through hyperedge HE-3 (induced by source paper "on rich clubs of path based centralities in networks"):
  with members:
    - "truss decomposition in massive networks"
    - "core periphery structure in networks"
    - "multiscale network generation"
    - "the rich club phenomenon in the internet topology"
    - "accurately modeling the internet topology"

Connected through hyperedge HE-4 (induced by source paper "core maintenance in dynamic graphs a parallel approach based on matching"):
  with members:
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"
    - "a model of internet topology using k shell decomposition"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-5 (induced by source paper "when engagement meets similarity efficient k r core computation on social networks"):
  with members:
    - "i o efficient core graph decomposition at web scale"
    - "towards realistic team formation in social networks based on densest subgraphs"
    - "keep your friends close and your facebook friends closer a multiplex network approach to the analysis of offline and online social ties"
    - "geo social group queries with minimum acquaintance constraint"
    - "preventing unraveling in social networks gets harder"
    - "parameterized complexity of the anchored k core problem for directed graphs"
    - "community detection in networks with node attributes"
    - "mining attribute structure correlated patterns in large attributed graphs"

Connected through hyperedge HE-6 (induced by source paper "fast hierarchy construction for dense subgraphs"):
  with members:
    - "i o efficient core graph decomposition at web scale"
    - "evolution of the internet k dense structure"
    - "truss decomposition in massive networks"
    - "social structure of facebook networks"
    - "a model of internet topology using k shell decomposition"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-7 (induced by source paper "parallel algorithms for core maintenance in dynamic graphs"):
  with members:
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"
    - "a model of internet topology using k shell decomposition"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-8 (induced by source paper "maxoutprobe an algorithm for increasing the size of partially observed networks"):
  with members:
    - "quick detection of high degree entities in large directed networks"
    - "romantic partnerships and the dispersion of social ties a network analysis of relationship status on facebook"

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)