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: "graph partition neural networks for semi supervised classification"
  Abstract: We present graph partition neural networks (GPNN), an extension of graph neural networks (GNNs) able to handle extremely large graphs. GPNNs alternate between locally propagating information between nodes in small subgraphs and globally propagating information between the subgraphs. To efficiently partition graphs, we experiment with several partitioning algorithms and also propose a novel variant for fast processing of large scale graphs. We extensively test our model on a variety of semi-supervised node classification tasks. Experimental results indicate that GPNNs are either superior or comparable to state-of-the-art methods on a wide variety of datasets for graph-based semi-supervised classification. We also show that GPNNs can achieve similar performance as standard GNNs with fewer propagation steps.

Connected through hyperedge HE-1 (induced by source paper "nonlinear equation solving a faster alternative to feedforward computation"):
  with members:
    - "pytorch an imperative style high performance deep learning library"
    - "fast generation for convolutional autoregressive models"
    - "attention is all you need"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "conditional image generation with pixelcnn decoders"
    - "densely connected convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "neural graph embedding methods for natural language processing"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "confidence based graph convolutional networks for semi supervised learning"
    - "hypernetwork knowledge graph embeddings"
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "lovasz convolutional networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "lanczosnet multi scale deep graph convolutional networks"):
  with members:
    - "graph networks as learnable physics engines for inference and control"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "junction tree variational autoencoder for molecular graph generation"
    - "inference in probabilistic graphical models by graph neural networks"
    - "situation recognition with graph neural networks"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-4 (induced by source paper "label efficient semi supervised learning via graph filtering"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "graph neural networks a review of methods and applications"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "deep learning on graphs a survey"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"

Connected through hyperedge HE-5 (induced by source paper "break the ceiling stronger multi scale deep graph convolutional networks"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "multi stage self supervised learning for graph convolutional networks"
    - "a comprehensive survey on graph neural networks"
    - "virtual adversarial training on graph convolutional networks in node classification"
    - "adaptive graph convolutional neural networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-6 (induced by source paper "confidence based graph convolutional networks for semi supervised learning"):
  with members:
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "lovasz convolutional networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "geometric deep learning going beyond euclidean data"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-7 (induced by source paper "graph convolutional networks analysis improvements and results"):
  with members:
    - "confidence based graph convolutional networks for semi supervised learning"
    - "learning combinations of activation functions"
    - "dual primal graph convolutional networks"
    - "lovasz convolutional networks"
    - "large scale learnable graph convolutional networks"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "learning a static bug finder from data"):
  with members:
    - "learning scalable and precise representation of program semantics"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "learning to represent programs with graphs"
    - "dynamic neural program embedding for program repair"
    - "gated graph sequence neural networks"

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)