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: "sengen sentence generating neural variational topic model"
  Abstract: We present a new topic model that generates documents by sampling a topic for one whole sentence at a time, and generating the words in the sentence using an RNN decoder that is conditioned on the topic of the sentence. We argue that this novel formalism will help us not only visualize and model the topical discourse structure in a document better, but also potentially lead to more interpretable topics since we can now illustrate topics by sampling representative sentences instead of bag of words or phrases. We present a variational auto-encoder approach for learning in which we use a factorized variational encoder that independently models the posterior over topical mixture vectors of documents using a feed-forward network, and the posterior over topic assignments to sentences using an RNN. Our preliminary experiments on two different datasets indicate early promise, but also expose many challenges that remain to be addressed.

Connected through hyperedge HE-1 (induced by source paper "unsupervised machine commenting with neural variational topic model"):
  with members:
    - "automatic article commenting the task and dataset"
    - "query and output generating words by querying distributed word representations for paraphrase generation"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "clustering is efficient for approximate maximum inner product search"
    - "neural variational inference for text processing"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "quantization based fast inner product search"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "iterative document representation learning towards summarization with polishing"):
  with members:
    - "learning to extract coherent summary via deep reinforcement learning"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "get to the point summarization with pointer generator networks"
    - "hybrid memnet for extractive summarization"
    - "neural summarization by extracting sentences and words"
    - "dynamic memory networks for visual and textual question answering"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "tensorflow a system for large scale machine learning"

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.CL(Computation and Language)