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: "simplicial closure and higher order link prediction"
  Abstract: Networks provide a powerful formalism for modeling complex systems by using a model of pairwise interactions. But much of the structure within these systems involves interactions that take place among more than two nodes at once—for example, communication within a group rather than person to person, collaboration among a team rather than a pair of coauthors, or biological interaction between a set of molecules rather than just two. Such higher-order interactions are ubiquitous, but their empirical study has received limited attention, and little is known about possible organizational principles of such structures. Here we study the temporal evolution of 19 datasets with explicit accounting for higher-order interactions. We show that there is a rich variety of structure in our datasets but datasets from the same system types have consistent patterns of higher-order structure. Furthermore, we find that tie strength and edge density are competing positive indicators of higher-order organization, and these trends are consistent across interactions involving differing numbers of nodes. To systematically further the study of theories for such higher-order structures, we propose higher-order link prediction as a benchmark problem to assess models and algorithms that predict higher-order structure. We find a fundamental difference from traditional pairwise link prediction, with a greater role for local rather than long-range information in predicting the appearance of new interactions.

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"
    - "motifs in temporal networks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "higher order organization of complex networks"

Connected through hyperedge HE-3 (induced by source paper "hypergraph cuts with general splitting functions"):
  with members:
    - "nonlinearity networks a 2020 vision"
    - "three hypergraph eigenvector centralities"
    - "network flow based refinement for multilevel hypergraph partitioning"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "higher order organization of complex networks"
    - "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-4 (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"
    - "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-5 (induced by source paper "how much and when do we need higher order information in hypergraphs a case study on hyperedge prediction"):
  with members:
    - "language in our time an empirical analysis of hashtags"
    - "hypergraph neural networks"
    - "is saki delicious the food perception gap on instagram and its relation to health"
    - "node2vec scalable feature learning for networks"
    - "uniform hypergraph partitioning provable tensor methods and sampling techniques"
    - "learning hypergraph regularized attribute predictors"
    - "predicting multi actor collaborations using hypergraphs"

Connected through hyperedge HE-6 (induced by source paper "edge corona product as an approach to modeling complex simplical networks"):
  with members:
    - "the simplex geometry of graphs"
    - "scale free loopy structure is resistant to noise in consensus dynamics in complex networks"
    - "extended corona product as an exactly tractable model for weighted heterogeneous networks"
    - "maximum matchings and minimum dominating sets in apollonian networks and extended tower of hanoi graphs"
    - "deterministic hierarchical networks"

Connected through hyperedge HE-7 (induced by source paper "powerset convolutional neural networks"):
  with members:
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "on the limitations of representing functions on sets"
    - "learning a sat solver from single bit supervision"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "deep sets"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "neural message passing for quantum chemistry"
    - "robust spatial filtering with graph convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "retrieving top weighted triangles in graphs"):
  with members:
    - "graph based semi supervised active learning for edge flows"
    - "tiered sampling an efficient method for approximate counting sparse motifs in massive graph streams"
    - "edge based wedge sampling to estimate triangle counts in very large graphs"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "higher order organization of complex networks"
    - "a fast and provable method for estimating clique counts using tur an s theorem"
    - "approximately counting triangles in sublinear time"
    - "role discovery in 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)