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: "inhomogoenous hypergraph clustering with applications"
  Abstract: Hypergraph partitioning is an important problem in machine learning, computer vision and network analytics. A widely used method for hypergraph partitioning relies on minimizing a normalized sum of the costs of partitioning hyperedges across clusters. Algorithmic solutions based on this approach assume that different partitions of a hyperedge incur the same cost. However, this assumption fails to leverage the fact that different subsets of vertices within the same hyperedge may have different structural importance. We hence propose a new hypergraph clustering technique, termed inhomogeneous hypergraph partitioning, which assigns different costs to different hyperedge cuts. We prove that inhomogeneous partitioning produces a quadratic approximation to the optimal solution if the inhomogeneous costs satisfy submodularity constraints. Moreover, we demonstrate that inhomogenous partitioning offers significant performance improvements in applications such as structure learning of rankings, subspace segmentation and motif clustering.

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 "hypergraph convolution and hypergraph attention"):
  with members:
    - "link prediction based on graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graph neural networks a review of methods and applications"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "adversarial attack on graph structured data"
    - "graph convolutional neural networks for web scale recommender systems"
    - "constrained graph variational autoencoders for molecule design"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-4 (induced by source paper "clustering in graphs and hypergraphs with categorical edge labels"):
  with members:
    - "nonlinearity networks a 2020 vision"
    - "a framework for second order eigenvector centralities and clustering coefficients"
    - "solving linear programs in the current matrix multiplication time"
    - "three hypergraph eigenvector centralities"
    - "motif and hypergraph correlation clustering"
    - "simplicial closure and higher order link prediction"
    - "correlation clustering generalized"
    - "spectral sparsification of simplicial complexes for clustering and label propagation"

Connected through hyperedge HE-5 (induced by source paper "quadratic decomposable submodular function minimization theory and practice"):
  with members:
    - "finding cheeger cuts in hypergraphs via heat equation"
    - "polynomial time algorithms for submodular laplacian systems"
    - "revisiting decomposable submodular function minimization with incidence relations"
    - "motif and hypergraph correlation clustering"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "decomposable submodular function minimization discrete and continuous"
    - "greedy algorithms for cone constrained optimization with convergence guarantees"
    - "cheeger inequalities for submodular transformations"

Connected through hyperedge HE-6 (induced by source paper "landing probabilities of random walks for seed set expansion in hypergraphs"):
  with members:
    - "random walks on hypergraphs with edge dependent vertex weights"
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "optimizing generalized pagerank methods for seed expansion community detection"
    - "quadratic decomposable submodular function minimization"
    - "the spacey random walk a stochastic process for higher order data"
    - "block models and personalized pagerank"
    - "higher order organization of complex networks"
    - "motif clustering and overlapping clustering for social network analysis"

Connected through hyperedge HE-7 (induced by source paper "integrating tensor similarity to enhance clustering performance"):
  with members:
    - "uniform hypergraph partitioning provable tensor methods and sampling techniques"
    - "context aware hypergraph construction for robust spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"):
  with members:
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "revisiting decomposable submodular function minimization with incidence relations"
    - "hypergcn a new method of training graph convolutional networks on hypergraphs"
    - "quadratic decomposable submodular function minimization"
    - "submodularity on hypergraphs from sets to sequences"
    - "cheeger inequalities for submodular transformations"
    - "computing minimum cuts in hypergraphs"
    - "higher order organization of complex 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.LG(Machine Learning)