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: "learning graph level representations with gated recurrent neural networks"
  Abstract: Recently a variety of methods have been developed to encode graphs into low-dimensional vectors that can be easily exploited by machine learning algorithms. The majority of these methods start by embedding the graph nodes into a low-dimensional vector space, followed by using some scheme to aggregate the node embeddings. In this work, we develop a new approach to learn graph-level representations, which includes a combination of unsupervised and supervised learning components. We start by learning a set of node representations in an unsupervised fashion. Graph nodes are mapped into node sequences sampled from random walk approaches approximated by the Gumbel-Softmax distribution. Recurrent neural network (RNN) units are modified to accommodate both the node representations as well as their neighborhood information. Experiments on standard graph classification benchmarks demonstrate that our proposed approach achieves superior or comparable performance relative to the state-of-the-art algorithms in terms of convergence speed and classification accuracy. We further illustrate the effectiveness of the different components used by our approach.

Connected through hyperedge HE-1 (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"
    - "kernel graph convolutional neural networks"
    - "high order stochastic graphlet embedding for graph based pattern recognition"
    - "graph2vec learning distributed representations of graphs"
    - "variational autoencoders for semi supervised text classification"
    - "feature based classification of networks"

Connected through hyperedge HE-2 (induced by source paper "network summarization with preserved spectral properties"):
  with members:
    - "network density of states"
    - "a unifying framework for spectrum preserving graph sparsification and coarsening"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "solving graph compression via optimal transport"
    - "netlsd hearing the shape of a graph"
    - "how powerful are graph neural networks"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"

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"
    - "kernel graph convolutional neural networks"
    - "high order stochastic graphlet embedding for graph based pattern recognition"
    - "feature based classification of 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)