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 organization of complex networks"
  Abstract: Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks—at the level of small network subgraphs—remains largely unknown. Here, we develop a generalized framework for clustering networks on the basis of higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in a number of networks, including information propagation units in neuronal networks and hub structure in transportation networks. Results show that networks exhibit rich higher-order organizational structures that are exposed by clustering based on higher-order connectivity patterns.

Connected through hyperedge HE-1 (induced by source paper "hypergraph motifs concepts algorithms and discoveries"):
  with members:
    - "how much and when do we need higher order information in hypergraphs a case study on hyperedge prediction"
    - "heterogeneous network motifs"
    - "edmot an edge enhancement approach for motif aware community detection"
    - "hypergraph clustering with categorical edge labels"
    - "motivo fast motif counting via succinct color coding and adaptive sampling"
    - "mining frequent patterns in evolving graphs"
    - "rept a streaming algorithm of approximating global and local triangle counts in parallel"
    - "simplicial closure and higher order link prediction"

Connected through hyperedge HE-2 (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"
    - "higher order clustering in networks"
    - "motif and hypergraph correlation clustering"
    - "correlation clustering generalized"

Connected through hyperedge HE-3 (induced by source paper "finding temporal patterns using algebraic fingerprints"):
  with members:
    - "approximately counting and sampling small witnesses using a colourful decision oracle"
    - "the computational complexity of finding temporal paths under waiting time constraints"
    - "motivo fast motif counting via succinct color coding and adaptive sampling"
    - "mining frequent patterns in evolving graphs"
    - "motifs in temporal networks"
    - "a motif based approach for identifying controversy"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "on the complexity of rainbow coloring problems"

Connected through hyperedge HE-4 (induced by source paper "localized flow based clustering in hypergraphs"):
  with members:
    - "hypergraph cuts with general splitting functions"
    - "landing probabilities of random walks for seed set expansion in hypergraphs"
    - "random walks on hypergraphs with edge dependent vertex weights"
    - "revisiting decomposable submodular function minimization with incidence relations"
    - "simplicial closure and higher order link prediction"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "flow based local graph clustering with better seed set inclusion"
    - "decomposable submodular function minimization discrete and continuous"

Connected through hyperedge HE-5 (induced by source paper "hypergraph cuts with general splitting functions"):
  with members:
    - "nonlinearity networks a 2020 vision"
    - "three hypergraph eigenvector centralities"
    - "simplicial closure and higher order link prediction"
    - "network flow based refinement for multilevel hypergraph partitioning"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "a simple and strongly local flow based method for cut improvement"
    - "simple and fast rounding algorithms for directed and node weighted multiway cut"
    - "local distribution and the symmetry gap approximability of multiway partitioning problems"

Connected through hyperedge HE-6 (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"
    - "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-7 (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"
    - "higher order clustering in networks"
    - "simplicial closure and higher order link prediction"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"

Connected through hyperedge HE-8 (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"
    - "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"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"

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)