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: "unsupervised inductive graph level representation learning via graph graph proximity"
  Abstract: We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity. Our approach, UGRAPHEMB, is a general framework that provides a novel means to performing graph-level embedding in a completely unsupervised and inductive manner. The learned neural network can be considered as a function that receives any graph as input, either seen or unseen in the training set, and transforms it into an embedding. A novel graph-level embedding generation mechanism called Multi-Scale Node Attention (MSNA), is proposed. Experiments on five real graph datasets show that UGRAPHEMB achieves competitive accuracy in the tasks of graph classification, similarity ranking, and graph visualization.

Connected through hyperedge HE-1 (induced by source paper "pre training graph neural networks"):
  with members:
    - "position aware graph neural networks"
    - "fast graph representation learning with pytorch geometric"
    - "analyzing learned molecular representations for property prediction"
    - "using pre training can improve model robustness and uncertainty"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "modeling polypharmacy side effects with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "a quotient space formulation for statistical analysis of graphical data"):
  with members:
    - "harp hierarchical representation learning for networks"
    - "graph2vec learning distributed representations of graphs"
    - "node2vec scalable feature learning for networks"
    - "predicting user roles in social networks using transfer learning with feature transformation"
    - "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-3 (induced by source paper "function space pooling for graph convolutional networks"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "how powerful are graph neural networks"
    - "neural message passing for quantum chemistry"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional networks on graphs for learning molecular fingerprints"

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)