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: "hypergcn a new method of training graph convolutional networks on hypergraphs"
  Abstract: In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise. Hypergraphs provide a flexible and natural modeling tool to model such complex relationships. The obvious existence of such complex relationships in many real-world networks naturaly motivates the problem of learning with hypergraphs. A popular learning paradigm is hypergraph-based semi-supervised learning (SSL) where the goal is to assign labels to initially unlabeled vertices in a hypergraph. Motivated by the fact that a graph convolutional network (GCN) has been effective for graph-based SSL, we propose HyperGCN, a novel GCN for SSL on attributed hypergraphs. Additionally, we show how HyperGCN can be used as a learning-based approach for combinatorial optimisation on NP-hard hypergraph problems. We demonstrate HyperGCN's effectiveness through detailed experimentation on real-world hypergraphs.

Connected through hyperedge HE-1 (induced by source paper "line hypergraph convolution network applying graph convolution for hypergraphs"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "dual primal graph convolutional networks"
    - "hypergraph neural networks"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "representation learning on graphs methods and applications"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"

Connected through hyperedge HE-2 (induced by source paper "hyper sagnn a self attention based graph neural network for hypergraphs"):
  with members:
    - "hypergraph convolution and hypergraph attention"
    - "hypergraph neural networks"
    - "attention is all you need"
    - "representation learning on graphs methods and applications"
    - "structural deep embedding for hyper networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "knowledge hypergraphs prediction beyond binary relations"):
  with members:
    - "hypernetwork knowledge graph embeddings"
    - "improved knowledge graph embedding using background taxonomic information"
    - "hypergraph neural networks"
    - "how powerful are graph neural networks"
    - "knowledge graph completion via complex tensor factorization"
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "knowledge base completion baselines strike back"
    - "convolutional 2d knowledge graph embeddings"

Connected through hyperedge HE-4 (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"
    - "quadratic decomposable submodular function minimization"
    - "submodularity on hypergraphs from sets to sequences"
    - "inhomogoenous hypergraph clustering with applications"
    - "cheeger inequalities for submodular transformations"
    - "computing minimum cuts in hypergraphs"
    - "higher order organization of complex networks"

Connected through hyperedge HE-5 (induced by source paper "revisiting decomposable submodular function minimization with incidence relations"):
  with members:
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "decomposable submodular function minimization discrete and continuous"
    - "inhomogoenous hypergraph clustering with applications"
    - "computing minimum cuts in hypergraphs"
    - "semi supervised classification with graph convolutional networks"
    - "random coordinate descent methods for minimizing decomposable submodular functions"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "scalable variational inference in log supermodular models"

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)