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: "motif based convolutional neural network on graphs"
  Abstract: This paper introduces a generalization of Convolutional Neural Networks (CNNs) to graphs with irregular linkage structures, especially heterogeneous graphs with typed nodes and schemas. We propose a novel spatial convolution operation to model the key properties of local connectivity and translation invariance, using high-order connection patterns or motifs. We develop a novel deep architecture Motif-CNN that employs an attention model to combine the features extracted from multiple patterns, thus effectively capturing high-order structural and feature information. Our experiments on semi-supervised node classification on real-world social networks and multiple representative heterogeneous graph datasets indicate significant gains of 6-21% over existing graph CNNs and other state-of-the-art techniques.

Connected through hyperedge HE-1 (induced by source paper "inf vae a variational autoencoder framework to integrate homophily and influence in diffusion prediction"):
  with members:
    - "an induced multi relational framework for answer selection in community question answer platforms"
    - "deepinf social influence prediction with deep learning"
    - "dynamic graph representation learning via self attention networks"
    - "attention is all you need"
    - "representation learning on graphs methods and applications"
    - "topological recurrent neural network for diffusion prediction"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional 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 "motif2vec motif aware node representation learning for heterogeneous networks"):
  with members:
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "graph embedding techniques applications and performance a survey"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "harp hierarchical representation learning for networks"
    - "higher order organization of complex networks"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-5 (induced by source paper "matching convolutional neural networks without priors about data"):
  with members:
    - "topology adaptive graph convolutional networks"
    - "convolutional neural networks on irregular domains through approximate translations on inferred graphs"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "a neighborhood preserving translation operator on graphs"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-6 (induced by source paper "graph convolutional neural networks via motif based attention"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "higher order clustering in networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "anonymous walk embeddings"
    - "covariant compositional networks for learning graphs"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "higher order organization of complex networks"

Connected through hyperedge HE-7 (induced by source paper "higher order clustering in heterogeneous information networks"):
  with members:
    - "higher order clustering in networks"
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "simplicial closure and higher order link prediction"
    - "motifs in temporal networks"
    - "recurrent meta structure for robust similarity measure in heterogeneous information networks"
    - "higher order organization of complex networks"
    - "scalable motif aware graph clustering"

Connected through hyperedge HE-8 (induced by source paper "user guided clustering in heterogeneous information networks via motif based comprehensive transcription"):
  with members:
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "higher order spectral clustering for heterogeneous graphs"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "prep path based relevance from a probabilistic perspective in heterogeneous information networks"
    - "higher order organization of complex networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "tensor spectral clustering for partitioning higher order network structures"

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)