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: "finding the hierarchy of dense subgraphs using nucleus decompositions"
  Abstract: Finding dense substructures in a graph is a fundamental graph mining operation, with applications in bioinformatics, social networks, and visualization to name a few. Yet most standard formulations of this problem (like clique, quasiclique, k-densest subgraph) are NP-hard. Furthermore, the goal is rarely to find the "true optimum", but to identify many (if not all) dense substructures, understand their distribution in the graph, and ideally determine relationships among them. Current dense subgraph finding algorithms usually optimize some objective, and only find a few such subgraphs without providing any structural relations. We define the nucleus decomposition of a graph, which represents the graph as a forest of nuclei. Each nucleus is a subgraph where smaller cliques are present in many larger cliques. The forest of nuclei is a hierarchy by containment, where the edge density increases as we proceed towards leaf nuclei. Sibling nuclei can have limited intersections, which enables discovering overlapping dense subgraphs. With the right parameters, the nucleus decomposition generalizes the classic notions of k-cores and k-truss decompositions. We give provably efficient algorithms for nucleus decompositions, and empirically evaluate their behavior in a variety of real graphs. The tree of nuclei consistently gives a global, hierarchical snapshot of dense substructures, and outputs dense subgraphs of higher quality than other state-of-the-art solutions. Our algorithm can process graphs with tens of millions of edges in less than an hour.

Connected through hyperedge HE-1 (induced by source paper "the power of pivoting for exact clique counting"):
  with members:
    - "higher order clustering in networks"
    - "detecting strong ties using network motifs"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "higher order organization of complex networks"
    - "scalable motif aware graph clustering"
    - "clique counting in mapreduce algorithms and experiments"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "wedge sampling for computing clustering coefficients and triangle counts on large graphs"

Connected through hyperedge HE-2 (induced by source paper "a fast counting method for 6 motifs with low connectivity"):
  with members:
    - "motivo fast motif counting via succinct color coding and adaptive sampling"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "higher order organization of complex networks"
    - "scalable motif aware graph clustering"
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"
    - "distributed estimation of graph 4 profiles"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"

Connected through hyperedge HE-3 (induced by source paper "a survey of community search over big graphs"):
  with members:
    - "exploring communities in large profiled graphs"
    - "efficient algorithms for densest subgraph discovery"
    - "community discovery in dynamic networks a survey"
    - "pp dblp modeling and generating attributed public private networks with dblp"
    - "an optimal and progressive approach to online search of top k influential communities"
    - "local community detection in dynamic networks"
    - "diversified coherent core search on multi layer graphs"
    - "network community detection a review and visual survey"

Connected through hyperedge HE-4 (induced by source paper "efficiently counting vertex orbits of all 5 vertex subgraphs by evoke"):
  with members:
    - "heterogeneous network motifs"
    - "higher order clustering in networks"
    - "detecting strong ties using network motifs"
    - "wrs waiting room sampling for accurate triangle counting in real graph streams"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "higher order organization of complex networks"
    - "a fast and provable method for estimating clique counts using tur an s theorem"
    - "scalable motif aware graph clustering"

Connected through hyperedge HE-5 (induced by source paper "linear time subgraph counting graph degeneracy and the chasm at size six"):
  with members:
    - "a simple sublinear time algorithm for counting arbitrary subgraphs via edge sampling"
    - "homomorphisms are a good basis for counting small subgraphs"
    - "on approximating the number of k cliques in sublinear time"
    - "combinatorial algorithm for counting small induced graphs and orbits"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "approximately counting triangles in sublinear time"
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"
    - "distributed estimation of graph 4 profiles"

Connected through hyperedge HE-6 (induced by source paper "efficient algorithms for densest subgraph discovery"):
  with members:
    - "exploring communities in large profiled graphs"
    - "a survey of community search over big graphs"
    - "local algorithms for hierarchical dense subgraph discovery"
    - "vertex priority based butterfly counting for large scale bipartite networks"
    - "the ubiquity of large graphs and surprising challenges of graph processing"
    - "density friendly graph decomposition"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "mathematical and algorithmic analysis of network and biological data"

Connected through hyperedge HE-7 (induced by source paper "distance generalized core decomposition"):
  with members:
    - "mining maximal span cores from temporal networks"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "density friendly graph decomposition"
    - "the minimum wiener connector problem"
    - "social resilience in online communities the autopsy of friendster"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "truss decomposition in massive networks"
    - "four degrees of separation"

Connected through hyperedge HE-8 (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"
    - "found graph data and planted vertex covers"
    - "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"
    - "community detection in networks with node attributes"

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)