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: "hypergraph neural networks"
  Abstract: In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-the-art methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods.

Connected through hyperedge HE-1 (induced by source paper "cross gcn enhancing graph convolutional network with k order feature interactions"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "graph representation learning via hard and channel wise attention networks"
    - "towards better text understanding and retrieval through kernel entity salience modeling"
    - "graph convolutional neural networks for web scale recommender systems"
    - "graph convolutional networks for text classification"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "deepinf social influence prediction with deep learning"
    - "learning structural node embeddings via diffusion wavelets"

Connected through hyperedge HE-2 (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"
    - "hypergcn a new method of training graph convolutional networks on hypergraphs"
    - "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-3 (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"
    - "simplicial closure and higher order link prediction"
    - "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-4 (induced by source paper "multiple discrimination and pairwise cnn for view based 3d object retrieval"):
  with members:
    - "contrastive center loss for deep neural networks"
    - "gift a real time and scalable 3d shape search engine"
    - "sketch based 3d shape retrieval using convolutional neural networks"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "a gentle introduction to deep learning for graphs"):
  with members:
    - "deep tree transductions a short survey"
    - "gmnn graph markov neural networks"
    - "a model to search for synthesizable molecules"
    - "graph u nets"
    - "a fair comparison of graph neural networks for graph classification"
    - "self attention graph pooling"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "fast and deep graph neural networks"

Connected through hyperedge HE-6 (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-7 (induced by source paper "graph representation learning a survey"):
  with members:
    - "learning graph pooling and hybrid convolutional operations for text representations"
    - "hierarchical graph convolutional networks for semi supervised node classification"
    - "interpretable convolutional neural networks via feedforward design"
    - "graph based deep tree recursive neural network dtrnn for text classification"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "mile a multi level framework for scalable graph embedding"
    - "attention models in graphs a survey"

Connected through hyperedge HE-8 (induced by source paper "explaining deep learning based networked systems"):
  with members:
    - "neural packet classification"
    - "hyper sagnn a self attention based graph neural network for hypergraphs"
    - "tensorflow js machine learning for the web and beyond"
    - "pforest in network inference with random forests"
    - "interpreting and improving natural language processing in machines with natural language processing in the brain"
    - "fooling neural network interpretations via adversarial model manipulation"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "mmalfm explainable recommendation by leveraging reviews and images"

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)