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: "high order stochastic graphlet embedding for graph based pattern recognition"
  Abstract: Graph-based methods are known to be successful for pattern description and comparison purpose. However, a lot of mathematical tools are unavailable in graph domain, thus restricting the generic graph-based techniques to be applicable within the machine learning framework. A way to tackle this problem is graph embedding into high dimensional space in either an explicit or implicit manner. In this paper, we propose high order stochastic graphlet embedding (SGE) that explicitly embed a graph into a real vector space. Our main contribution includes a new stochastic search procedure that allows one to efficiently parse a given graph and extract or sample unlimitedly high order graphlets. We consider these graphlets with increasing size in order to model local features, as well as, their complex interactions. We also introduce or design graph hash functions with very low probability of collision to hash those sampled graphlets for partitioning them into sets of isomorphic ones and measure their distribution in large graph collections, which results in accurate graph descriptions. When combined with support vector machines, these high order graphlet-based descriptions have positive impact on the performance of graph-based pattern comparison and classification as corroborated through experiments on different standard benchmark databases.

Connected through hyperedge HE-1 (induced by source paper "hierarchical stochastic graphlet embedding for graph based pattern recognition"):
  with members:
    - "stochastic graphlet embedding"
    - "improved algorithm on online clustering of bandits"
    - "neural message passing for quantum chemistry"
    - "product graph based higher order contextual similarities for inexact subgraph matching"
    - "distributed clustering of linear bandits in peer to peer 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-2 (induced by source paper "variational recurrent neural networks for graph classification"):
  with members:
    - "a simple baseline algorithm for graph classification"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning graph level representations with gated recurrent neural networks"
    - "kernel graph convolutional neural networks"
    - "graph2vec learning distributed representations of graphs"
    - "variational autoencoders for semi supervised text classification"
    - "feature based classification of networks"

Connected through hyperedge HE-3 (induced by source paper "a simple baseline algorithm for graph classification"):
  with members:
    - "mixed batches and symmetric discriminators for gan training"
    - "weighted spectral embedding of graphs"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning graph level representations with gated recurrent neural networks"
    - "kernel graph convolutional neural networks"
    - "feature based classification of networks"

Connected through hyperedge HE-4 (induced by source paper "motif based convolutional neural network on graphs"):
  with members:
    - "dynamic graph representation learning via self attention networks"
    - "attention is all you need"
    - "improving latent user models in online social media"
    - "detecting strong ties using network motifs"
    - "robust spatial filtering with graph convolutional neural networks"
    - "higher order organization of complex networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for 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.CV(Computer Vision and Pattern Recognition)