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: "higher order clustering in networks"
  Abstract: A fundamental property of complex networks is the tendency for edges to cluster. The extent of the clustering is typically quantified by the clustering coefficient, which is the probability that a length-2 path is closed, i.e., induces a triangle in the network. However, higher-order cliques beyond triangles are crucial to understanding complex networks, and the clustering behavior with respect to such higher-order network structures is not well understood. Here we introduce higher-order clustering coefficients that measure the closure probability of higher-order network cliques and provide a more comprehensive view of how the edges of complex networks cluster. Our higher-order clustering coefficients are a natural generalization of the traditional clustering coefficient. We derive several properties about higher-order clustering coefficients and analyze them under common random graph models. Finally, we use higher-order clustering coefficients to gain new insights into the structure of real-world networks from several domains.

Connected through hyperedge HE-1 (induced by source paper "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs"):
  with members:
    - "hypergraph cuts with general splitting functions"
    - "clustering in graphs and hypergraphs with categorical edge labels"
    - "learning resolution parameters for graph clustering"
    - "a parallel projection method for metric constrained optimization"
    - "hypergraph clustering with categorical edge labels"
    - "motif and hypergraph correlation clustering"
    - "correlation clustering generalized"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "the power of pivoting for exact clique counting"):
  with members:
    - "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"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"
    - "wedge sampling for computing clustering coefficients and triangle counts on large graphs"

Connected through hyperedge HE-3 (induced by source paper "higher order label homogeneity and spreading in graphs"):
  with members:
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "higher order ranking and link prediction from closing triangles to closing higher order motifs"
    - "simplicial closure and higher order link prediction"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"

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"
    - "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"
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"

Connected through hyperedge HE-5 (induced by source paper "a framework for second order eigenvector centralities and clustering coefficients"):
  with members:
    - "three hypergraph eigenvector centralities"
    - "motif and hypergraph correlation clustering"
    - "simplicial closure and higher order link prediction"
    - "node and layer eigenvector centralities for multiplex networks"
    - "higher order organization of complex networks"
    - "pagerank beyond the web"
    - "predicting triadic closure in networks using communicability distance functions"

Connected through hyperedge HE-6 (induced by source paper "graph convolutional neural networks via motif based attention"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "hierarchical graph representation learning with differentiable pooling"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "anonymous walk embeddings"
    - "covariant compositional networks for learning graphs"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "motif based convolutional neural network on graphs"
    - "higher order organization of complex networks"

Connected through hyperedge HE-7 (induced by source paper "higher order clustering in heterogeneous information networks"):
  with members:
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "simplicial closure and higher order link prediction"
    - "motifs in temporal networks"
    - "recurrent meta structure for robust similarity measure in heterogeneous information networks"
    - "motif based convolutional neural network on graphs"
    - "higher order organization of complex networks"
    - "scalable motif aware graph clustering"

Connected through hyperedge HE-8 (induced by source paper "random spatial network models with core periphery structure"):
  with members:
    - "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"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"
    - "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)