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: "improving semantic relevance for sequence to sequence learning of chinese social media text summarization"
  Abstract: Current Chinese social media text summarization models are based on an encoder-decoder framework. Although its generated summaries are similar to source texts literally, they have low semantic relevance. In this work, our goal is to improve semantic relevance between source texts and summaries for Chinese social media summarization. We introduce a Semantic Relevance Based neural model to encourage high semantic similarity between texts and summaries. In our model, the source text is represented by a gated attention encoder, while the summary representation is produced by a decoder. Besides, the similarity score between the representations is maximized during training. Our experiments show that the proposed model outperforms baseline systems on a social media corpus.

Connected through hyperedge HE-1 (induced by source paper "co attention hierarchical network generating coherent long distractors for reading comprehension"):
  with members:
    - "generating distractors for reading comprehension questions from real examinations"
    - "race large scale reading comprehension dataset from examinations"
    - "get to the point summarization with pointer generator networks"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "dynamic coattention networks for question answering"
    - "a hierarchical neural autoencoder for paragraphs and documents"

Connected through hyperedge HE-2 (induced by source paper "dp gan diversity promoting generative adversarial network for generating informative and diversified text"):
  with members:
    - "global encoding for abstractive summarization"
    - "autoencoder as assistant supervisor improving text representation for chinese social media text summarization"
    - "bag of words as target for neural machine translation"
    - "generating sentences by editing prototypes"
    - "began boundary equilibrium generative adversarial networks"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "adversarial learning for neural dialogue generation"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "describing a knowledge base"):
  with members:
    - "learning to generate wikipedia summaries for underserved languages from wikidata"
    - "paper abstract writing through editing mechanism"
    - "a graph to sequence model for amr to text generation"
    - "entity aware image caption generation"
    - "structured attention networks"
    - "learning structured text representations"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-4 (induced by source paper "abstractive summarization improved by wordnet based extractive sentences"):
  with members:
    - "selective encoding for abstractive sentence summarization"
    - "efficient summarization with read again and copy mechanism"
    - "diversity driven attention model for query based abstractive summarization"
    - "get to the point summarization with pointer generator networks"
    - "a deep reinforced model for abstractive summarization"
    - "pointing the unknown words"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-5 (induced by source paper "autoencoder as assistant supervisor improving text representation for chinese social media text summarization"):
  with members:
    - "query and output generating words by querying distributed word representations for paraphrase generation"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "get to the point summarization with pointer generator networks"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural summarization by extracting sentences and words"
    - "a neural attention model for abstractive sentence summarization"
    - "a hierarchical neural autoencoder for paragraphs and documents"

Connected through hyperedge HE-6 (induced by source paper "word embedding attention network generating words by querying distributed word representations for paraphrase generation"):
  with members:
    - "decoding history based adaptive control of attention for neural machine translation"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "table to text generation by structure aware seq2seq learning"
    - "joint copying and restricted generation for paraphrase"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural summarization by extracting sentences and words"
    - "neural paraphrase generation with stacked residual lstm networks"

Connected through hyperedge HE-7 (induced by source paper "label embedding network learning label representation for soft training of deep networks"):
  with members:
    - "a semantic relevance based neural network for text summarization and text simplification"
    - "wide residual networks"
    - "all you need is a good init"
    - "deep residual learning for image recognition"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "training very deep networks"
    - "adam a method for stochastic optimization"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-8 (induced by source paper "evaluating semantic rationality of a sentence a sememe word matching neural network based on hownet"):
  with members:
    - "improving word vector with prior knowledge in semantic dictionary"
    - "sememe prediction learning semantic knowledge from unstructured textual wiki descriptions"
    - "a semantic relevance based neural network for text summarization and text simplification"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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)