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: "abstractive and extractive text summarization using document context vector and recurrent neural networks"
  Abstract: Sequence to sequence (Seq2Seq) learning has recently been used for abstractive and extractive summarization. In current study, Seq2Seq models have been used for eBay product description summarization. We propose a novel Document-Context based Seq2Seq models using RNNs for abstractive and extractive summarizations. Intuitively, this is similar to humans reading the title, abstract or any other contextual information before reading the document. This gives humans a high-level idea of what the document is about. We use this idea and propose that Seq2Seq models should be started with contextual information at the first time-step of the input to obtain better summaries. In this manner, the output summaries are more document centric, than being generic, overcoming one of the major hurdles of using generative models. We generate document-context from user-behavior and seller provided information. We train and evaluate our models on human-extracted-golden-summaries. The document-contextual Seq2Seq models outperform standard Seq2Seq models. Moreover, generating human extracted summaries is prohibitively expensive to scale, we therefore propose a semi-supervised technique for extracting approximate summaries and using it for training Seq2Seq models at scale. Semi-supervised models are evaluated against human extracted summaries and are found to be of similar efficacy. We provide side by side comparison for abstractive and extractive summarizers (contextual and non-contextual) on same evaluation dataset. Overall, we provide methodologies to use and evaluate the proposed techniques for large document summarization. Furthermore, we found these techniques to be highly effective, which is not the case with existing techniques.

Connected through hyperedge HE-1 (induced by source paper "neural abstractive text summarization and fake news detection"):
  with members:
    - "attending to entities for better text understanding"
    - "abstractive text summarization by incorporating reader comments"
    - "end to end alzheimer s disease diagnosis and biomarker identification"
    - "end to end parkinson disease diagnosis using brain mr images by 3d cnn"
    - "towards explainable nlp a generative explanation framework for text classification"
    - "wikihow a large scale text summarization dataset"
    - "text summarization techniques a brief survey"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "headline generation learning from decomposed document titles"):
  with members:
    - "neural abstractive text summarization and fake news detection"
    - "a novel repetition normalized adversarial reward for headline generation"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "text summarization techniques a brief survey"
    - "get to the point summarization with pointer generator networks"
    - "source side prediction for neural headline generation"
    - "neural headline generation with minimum risk training"

Connected through hyperedge HE-3 (induced by source paper "deepobfuscode source code obfuscation through sequence to sequence networks"):
  with members:
    - "attention based encoder decoder networks for spelling and grammatical error correction"
    - "cryptodl deep neural networks over encrypted data"
    - "obfuscation using encryption"
    - "assessment of source code obfuscation techniques"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)