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 compression as tree transduction"
  Abstract: This paper presents a tree-to-tree transduction method for sentence compression. Our model is based on synchronous tree substitution grammar, a formalism that allows local distortion of the tree topology and can thus naturally capture structural mismatches. We describe an algorithm for decoding in this framework and show how the model can be trained discriminatively within a large margin framework. Experimental results on sentence compression bring significant improvements over a state-of-the-art model.

Connected through hyperedge HE-1 (induced by source paper "deep tree transductions a short survey"):
  with members:
    - "text summarization as tree transduction by top down treelstm"
    - "hidden tree markov networks deep and wide learning for structured data"
    - "improved semantic representations from tree structured long short term memory networks"
    - "tree recurrent neural networks with application to language modeling"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "learning to perform local rewriting for combinatorial optimization"):
  with members:
    - "gamepad a learning environment for theorem proving"
    - "learning heuristics for automated reasoning through deep reinforcement learning"
    - "learning to optimize tensor programs"
    - "can neural networks understand logical entailment"
    - "learning of human like algebraic reasoning using deep feedforward neural networks"
    - "learning combinatorial optimization algorithms over graphs"
    - "reinforcement learning with deep energy based policies"
    - "deep reinforcement learning for multi resource multi machine job scheduling"

Connected through hyperedge HE-3 (induced by source paper "text summarization as tree transduction by top down treelstm"):
  with members:
    - "hidden tree markov networks deep and wide learning for structured data"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "end to end memory networks"
    - "improved semantic representations from tree structured long short term memory networks"
    - "tree recurrent neural networks with application to language modeling"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "neural summarization by extracting sentences and words"):
  with members:
    - "pointing the unknown words"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "a neural attention model for abstractive sentence summarization"
    - "character aware neural language models"
    - "phrase based image captioning"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "teaching machines to read and comprehend"
    - "on using monolingual corpora in neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "improving sentence compression by learning to predict gaze"):
  with members:
    - "bidirectional lstm crf models for sequence tagging"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "centrality as relevance support sets and similarity as geometric proximity"):
  with members:
    - "from frequency to meaning vector space models of semantics"
    - "pagerank without hyperlinks structural re ranking using links induced by language models"
    - "centroid based summarization of multiple documents sentence extraction utility based evaluation and user studies"

Connected through hyperedge HE-7 (induced by source paper "a survey of paraphrasing and textual entailment methods"):
  with members:
    - "text relatedness based on a word thesaurus"
    - "from frequency to meaning vector space models of semantics"
    - "acquiring lexical paraphrases from a single corpus"
    - "learning to paraphrase an unsupervised approach using multiple sequence alignment"
    - "bootstrapping lexical choice via multiple sequence alignment"
    - "semantic similarity in a taxonomy an information based measure and its application to problems of ambiguity in natural language"

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)