human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "variational recurrent neural networks for graph classification"):
  Abstract: We address the problem of graph classification based only on structural information. Inspired by natural language processing techniques (NLP), our model sequentially embeds information to estimate class membership probabilities. Besides, we experiment with NLP-like variational regularization techniques, making the model predict the next node in the sequence as it reads it. We experimentally show that our model achieves state-of-the-art classification results on several standard molecular datasets. Finally, we perform a qualitative analysis and give some insights on whether the node prediction helps the model better classify graphs.

Includes members:
  Member "a simple baseline algorithm for graph classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an api oriented open source python framework for unsupervised learning on graphs")
  Member "a hierarchical multi task approach for learning embeddings from semantic tasks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models")
  Member "graphrnn generating realistic graphs with deep auto regressive models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "learning graph level representations with gated recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "network summarization with preserved spectral properties")
  Member "kernel graph convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a kernel of truth determining rumor veracity on twitter by diffusion pattern alone")
  Member "high order stochastic graphlet embedding for graph based pattern recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchical stochastic graphlet embedding for graph based pattern recognition")
  Member "graph2vec learning distributed representations of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "few shot learning on graphs via super classes based on graph spectral measures")
  Member "variational autoencoders for semi supervised text classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning from explanations with neural execution tree")

Question: Please tell me which class the source paper 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)