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: "sentence rewriting for semantic parsing"
  Abstract: A major challenge of semantic parsing is the vocabulary mismatch problem between natural language and target ontology. In this paper, we propose a sentence rewriting based semantic parsing method, which can effectively resolve the mismatch problem by rewriting a sentence into a new form which has the same structure with its target logical form. Specifically, we propose two sentence-rewriting methods for two common types of mismatch: a dictionary-based method for 1-N mismatch and a template-based method for N-1 mismatch. We evaluate our entence rewriting based semantic parser on the benchmark semantic parsing dataset -- WEBQUESTIONS. Experimental results show that our system outperforms the base system with a 3.4% gain in F1, and generates logical forms more accurately and parses sentences more robustly.

Connected through hyperedge HE-1 (induced by source paper "improving multi turn dialogue modelling with utterance rewriter"):
  with members:
    - "review driven answer generation for product related questions in e commerce"
    - "cota improving the speed and accuracy of customer support through ranking and deep networks"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "an exploration of neural sequence to sequence architectures for automatic post editing"
    - "end to end neural coreference resolution"
    - "quickedit editing text translations by crossing words out"
    - "a knowledge grounded neural conversation model"

Connected through hyperedge HE-2 (induced by source paper "sequence to action end to end semantic graph generation for semantic parsing"):
  with members:
    - "learning to paraphrase for question answering"
    - "abstract syntax networks for code generation and semantic parsing"
    - "language to logical form with neural attention"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"
    - "learning dependency based compositional semantics"

Connected through hyperedge HE-3 (induced by source paper "learning a neural semantic parser from user feedback"):
  with members:
    - "language to logical form with neural attention"
    - "equation parsing mapping sentences to grounded equations"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "distributed representations of words and phrases and their compositionality"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"
    - "learning dependency based compositional semantics"

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)