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: "subgraph2vec learning distributed representations of rooted sub graphs from large graphs"
  Abstract: In this paper, we present subgraph2vec, a novel approach for learning latent representations of rooted subgraphs from large graphs inspired by recent advancements in Deep Learning and Graph Kernels. These latent representations encode semantic substructure dependencies in a continuous vector space, which is easily exploited by statistical models for tasks such as graph classification, clustering, link prediction and community detection. subgraph2vec leverages on local information obtained from neighbourhoods of nodes to learn their latent representations in an unsupervised fashion. We demonstrate that subgraph vectors learnt by our approach could be used in conjunction with classifiers such as CNNs, SVMs and relational data clustering algorithms to achieve significantly superior accuracies. Also, we show that the subgraph vectors could be used for building a deep learning variant of Weisfeiler-Lehman graph kernel. Our experiments on several benchmark and large-scale real-world datasets reveal that subgraph2vec achieves significant improvements in accuracies over existing graph kernels on both supervised and unsupervised learning tasks. Specifically, on two realworld program analysis tasks, namely, code clone and malware detection, subgraph2vec outperforms state-of-the-art kernels by more than 17% and 4%, respectively.

Connected through hyperedge HE-1 (induced by source paper "strategies for pre training graph neural networks"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "graph warp module an auxiliary module for boosting the power of graph neural networks"
    - "using pre training can improve model robustness and uncertainty"
    - "graph convolutional neural networks for web scale recommender systems"
    - "representation learning on graphs with jumping knowledge networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning representations of graph data a survey"):
  with members:
    - "fast sequence based embedding with diffusion graphs"
    - "adaptive graph convolutional neural networks"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "deriving neural architectures from sequence and graph kernels"
    - "inductive representation learning on large graphs"

Connected through hyperedge HE-3 (induced by source paper "from community to role based graph embeddings"):
  with members:
    - "node2bits compact time and attribute aware node representations for user stitching"
    - "similarity learning with higher order graph convolutions for brain network analysis"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "overlapping community detection with graph neural networks"
    - "feature propagation on graph a new perspective to graph representation learning"
    - "regal representation learning based graph alignment"
    - "graph embedding techniques applications and performance a survey"
    - "higher order graph convolutional networks"

Connected through hyperedge HE-4 (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"
    - "unsupervised inductive graph level representation learning via graph graph proximity"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "a unifying view of explicit and implicit feature maps of graph kernels"):
  with members:
    - "a survey on graph kernels"
    - "graph convolutional neural networks for web scale recommender systems"
    - "tree based kernel for graphs with continuous attributes"
    - "representation learning on graphs methods and applications"
    - "neural message passing for quantum chemistry"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "on valid optimal assignment kernels and applications to graph classification"

Connected through hyperedge HE-6 (induced by source paper "ego cnn distributed egocentric representations of graphs for detecting critical structures"):
  with members:
    - "hierarchical graph representation learning with differentiable pooling"
    - "neural message passing for quantum chemistry"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "column networks for collective classification"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-7 (induced by source paper "data curation with deep learning vision towards self driving data curation"):
  with members:
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep learning a critical appraisal"
    - "a survey of methods for explaining black box models"
    - "deeper deep entity resolution"
    - "mida multiple imputation using denoising autoencoders"
    - "unsupervised string transformation learning for entity consolidation"
    - "snorkel rapid training data creation with weak supervision"

Connected through hyperedge HE-8 (induced by source paper "data curation with deep learning vision"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "deeper deep entity resolution"
    - "snorkel rapid training data creation with weak supervision"
    - "holoclean holistic data repairs with probabilistic inference"
    - "a survey on network embedding"
    - "graph2vec learning distributed representations of graphs"

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)