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: "tensor spectral clustering for partitioning higher order network structures"
  Abstract: Spectral graph theory-based methods represent an important class of tools for studying the structure of networks. Spectral methods are based on a first-order Markov chain derived from a random walk on the graph and thus they cannot take advantage of important higher-order network substructures such as triangles, cycles, and feed-forward loops. Here we propose a Tensor Spectral Clustering (TSC) algorithm that allows for modeling higher-order network structures in a graph partitioning framework. Our TSC algorithm allows the user to specify which higher-order network structures (cycles, feed-forward loops, etc.) should be preserved by the network clustering. Higher-order network structures of interest are represented using a tensor, which we then partition by developing a multilinear spectral method. Our framework can be applied to discovering layered flows in networks as well as graph anomaly detection, which we illustrate on synthetic networks. In directed networks, a higher-order structure of particular interest is the directed 3-cycle, which captures feedback loops in networks. We demonstrate that our TSC algorithm produces large partitions that cut fewer directed 3-cycles than standard spectral clustering algorithms.

Connected through hyperedge HE-1 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite"):
  with members:
    - "load balanced sparse mttkrp on gpus"
    - "tensor train decomposition on tensorflow t3f"
    - "a practical randomized cp tensor decomposition"
    - "software for sparse tensor decomposition on emerging computing architectures"
    - "spartan scalable parafac2 for large sparse data"
    - "tensor regression meets gaussian processes"
    - "tensor computation a new framework for high dimensional problems in eda"
    - "a unified optimization approach for sparse tensor operations on gpus"

Connected through hyperedge HE-2 (induced by source paper "network embedding as matrix factorization unifying deepwalk line pte and node2vec"):
  with members:
    - "the spacey random walk a stochastic process for higher order data"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "a latent variable model approach to pmi based word embeddings"
    - "multi modal bayesian embeddings for learning social knowledge graphs"
    - "comprehend deepwalk as matrix factorization"
    - "pte predictive text embedding through large scale heterogeneous text networks"

Connected through hyperedge HE-3 (induced by source paper "random walks on simplicial complexes and the normalized hodge 1 laplacian"):
  with members:
    - "graph based semi supervised active learning for edge flows"
    - "flow smoothing and denoising graph signal processing in the edge space"
    - "simplicial closure and higher order link prediction"
    - "centralities in simplicial complexes"
    - "centrality measures for graphons accounting for uncertainty in networks"
    - "the many facets of community detection in complex networks"
    - "block models and personalized pagerank"
    - "towards spectral sparsification of simplicial complexes based on generalized effective resistance"

Connected through hyperedge HE-4 (induced by source paper "higher order clustering in heterogeneous information networks"):
  with members:
    - "higher order clustering in networks"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "user guided clustering in heterogeneous information networks via motif based comprehensive transcription"):
  with members:
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "higher order spectral clustering for heterogeneous graphs"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "prep path based relevance from a probabilistic perspective in heterogeneous information networks"
    - "motif based convolutional neural network on graphs"
    - "higher order organization of complex networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"

Connected through hyperedge HE-6 (induced by source paper "identification of overlapping communities via constrained egonet tensor decomposition"):
  with members:
    - "detecting overlapping communities from local spectral subspaces"
    - "a tensor approach to learning mixed membership community models"
    - "rank regularization and bayesian inference for tensor completion and extrapolation"
    - "online tensor methods for learning latent variable models"
    - "overlapping community detection in complex networks using symmetric binary matrix factorization"
    - "community detection in networks with node attributes"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "mixed order spectral clustering for networks"):
  with members:
    - "inhomogoenous hypergraph clustering with applications"
    - "higher order organization of complex networks"
    - "uniform hypergraph partitioning provable tensor methods and sampling techniques"
    - "metrics for community analysis a survey"
    - "clustering and community detection in directed networks a survey"
    - "defining and evaluating network communities based on ground truth"
    - "empirical comparison of algorithms for network community detection"

Connected through hyperedge HE-8 (induced by source paper "spectral sparsification of simplicial complexes for clustering and label propagation"):
  with members:
    - "near optimal sketching of low rank tensor regression"
    - "polynomial sized topological approximations using the permutahedron"
    - "pagerank beyond the web"
    - "graph induced complex on point data"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "computing topological persistence for simplicial maps"
    - "applications of zigzag persistence to topological data analysis"
    - "fast approximation of matrix coherence and statistical leverage"

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)