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: "a semantic relevance based neural network for text summarization and text simplification"
  Abstract: Text summarization and text simplification are two major ways to simplify the text for poor readers, including children, non-native speakers, and the functionally illiterate. Text summarization is to produce a brief summary of the main ideas of the text, while text simplification aims to reduce the linguistic complexity of the text and retain the original meaning. Recently, most approaches for text summarization and text simplification are based on the sequence-to-sequence model, which achieves much success in many text generation tasks. However, although the generated simplified texts 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 simplified texts for text summarization and text simplification. 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 the state-of-the-art systems on two benchmark corpus.

Connected through hyperedge HE-1 (induced by source paper "from plots to endings a reinforced pointer generator for story ending generation"):
  with members:
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "reinforced video captioning with entailment rewards"
    - "improved image captioning via policy gradient optimization of spider"
    - "video captioning via hierarchical reinforcement learning"
    - "sentence simplification with deep reinforcement learning"
    - "get to the point summarization with pointer generator networks"
    - "consensus based sequence training for video captioning"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-2 (induced by source paper "label embedding network learning label representation for soft training of deep networks"):
  with members:
    - "improving semantic relevance for sequence to sequence learning of chinese social media text summarization"
    - "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-3 (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"
    - "improving semantic relevance for sequence to sequence learning of chinese social media text summarization"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "bleu is not suitable for the evaluation of text simplification"):
  with members:
    - "simple and effective text simplification using semantic and neural methods"
    - "split and rephrase"
    - "a constrained sequence to sequence neural model for sentence simplification"

Connected through hyperedge HE-5 (induced by source paper "sentence correction based on large scale language modelling"):
  with members:
    - "improving semantic relevance for sequence to sequence learning of chinese social media text summarization"
    - "one billion word benchmark for measuring progress in statistical language modeling"

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)