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: "max margin nonparametric latent feature models for link prediction"
  Abstract: Link prediction is a fundamental task in statistical network analysis. Recent advances have been made on learning flexible nonparametric Bayesian latent feature models for link prediction. In this paper, we present a max-margin learning method for such nonparametric latent feature relational models. Our approach attempts to unite the ideas of max-margin learning and Bayesian nonparametrics to discover discriminative latent features for link prediction. It inherits the advances of nonparametric Bayesian methods to infer the unknown latent social dimension, while for discriminative link prediction, it adopts the max-margin learning principle by minimizing a hinge-loss using the linear expectation operator, without dealing with a highly nonlinear link likelihood function. For posterior inference, we develop an efficient stochastic variational inference algorithm under a truncated mean-field assumption. Our methods can scale up to large-scale real networks with millions of entities and tens of millions of positive links. We also provide a full Bayesian formulation, which can avoid tuning regularization hyper-parameters. Experimental results on a diverse range of real datasets demonstrate the benefits inherited from max-margin learning and Bayesian nonparametric inference.

Connected through hyperedge HE-1 (induced by source paper "multi stage self supervised learning for graph convolutional networks on graphs with few labels"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "graph networks as learnable physics engines for inference and control"
    - "deep clustering for unsupervised learning of visual features"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "interaction networks for learning about objects relations and physics"
    - "unsupervised visual representation learning by context prediction"

Connected through hyperedge HE-2 (induced by source paper "explainable product search with a dynamic relation embedding model"):
  with members:
    - "conversational product search based on negative feedback"
    - "leverage implicit feedback for context aware product search"
    - "a zero attention model for personalized product search"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "learning a deep listwise context model for ranking refinement"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "learning over knowledge base embeddings for recommendation"
    - "explainable recommendation via multi task learning in opinionated text data"

Connected through hyperedge HE-3 (induced by source paper "adagcn adaboosting graph convolutional networks into deep models"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "simplifying graph convolutional networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "pitfalls of graph neural network evaluation"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "n gcn multi scale graph convolution for semi supervised node classification"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "bootstrapped graph diffusions exposing the power of nonlinearity"

Connected through hyperedge HE-4 (induced by source paper "multi stage self supervised learning for graph convolutional networks"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "graph networks as learnable physics engines for inference and control"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "knowledge base completion with out of knowledge base entities a graph neural network approach"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-5 (induced by source paper "virtual adversarial training on graph convolutional networks in node classification"):
  with members:
    - "adversarial attacks on graph neural networks via meta learning"
    - "tangent normal adversarial regularization for semi supervised learning"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-6 (induced by source paper "stochastic blockmodels meet graph neural networks"):
  with members:
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "semi supervised classification with graph convolutional networks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"
    - "efficient gradient based inference through transformations between bayes nets and neural nets"
    - "sparse matrix variate gaussian process blockmodels for network modeling"

Connected through hyperedge HE-7 (induced by source paper "learning heterogeneous knowledge base embeddings for explainable recommendation"):
  with members:
    - "explainable recommendation a survey and new perspectives"
    - "translation based recommendation"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "explainable entity based recommendations with knowledge graphs"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "image based recommendations on styles and substitutes"
    - "distributed representations of sentences and documents"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-8 (induced by source paper "log normal matrix completion for large scale link prediction"):
  with members:
    - "pu learning for matrix completion"
    - "a convex formulation for learning scale free networks via submodular relaxation"
    - "estimation of simultaneously sparse and low rank matrices"
    - "nonparametric link prediction in dynamic networks"
    - "evolution of social attribute networks measurements modeling and implications using google"
    - "learning with submodular functions a convex optimization perspective"

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)