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 survey of paraphrasing and textual entailment methods"
  Abstract: Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.

Connected through hyperedge HE-1 (induced by source paper "visuallly grounded generation of entailments from premises"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "visual entailment a novel task for fine grained image understanding"
    - "quantifying the amount of visual information used by neural caption generators"
    - "annotation artifacts in natural language inference data"
    - "object counts bringing explicit detections back into image captioning"
    - "grounded textual entailment"
    - "where to put the image in an image caption generator"
    - "foil it find one mismatch between image and language caption"

Connected through hyperedge HE-2 (induced by source paper "heuristics for interpretable knowledge graph contextualization"):
  with members:
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "improving natural language inference using external knowledge in the science questions domain"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "neural natural language inference models enhanced with external knowledge"
    - "a decomposable attention model for natural language inference"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "learning natural language inference using bidirectional lstm model and inner attention"
    - "complex embeddings for simple link prediction"

Connected through hyperedge HE-3 (induced by source paper "how to ask better questions a large scale multi domain dataset for rewriting ill formed questions"):
  with members:
    - "translating web search queries into natural language questions"
    - "corpora generation for grammatical error correction"
    - "decomposable neural paraphrase generation"
    - "controllable paraphrase generation with a syntactic exemplar"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "identifying well formed natural language questions"
    - "learning to paraphrase for question answering"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"

Connected through hyperedge HE-4 (induced by source paper "having the last word understanding how to sample discussions online"):
  with members:
    - "tweeting mps digital engagement between citizens and members of parliament in the uk"
    - "notes on abstract argumentation theory"

Connected through hyperedge HE-5 (induced by source paper "a survey on why type question answering systems"):
  with members:
    - "natural language processing almost from scratch"
    - "practical approach to knowledge based question answering with natural language understanding and advanced reasoning"

Connected through hyperedge HE-6 (induced by source paper "iparaphrasing extracting visually grounded paraphrases via an image"):
  with members:
    - "using syntax to ground referring expressions in natural images"
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "end to end neural coreference resolution"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "modeling relationships in referential expressions with compositional modular networks"
    - "grounding of textual phrases in images by reconstruction"
    - "phrase localization and visual relationship detection with comprehensive image language cues"
    - "learning joint representations of videos and sentences with web image search"

Connected through hyperedge HE-7 (induced by source paper "adversarial example generation with syntactically controlled paraphrase networks"):
  with members:
    - "deep text classification can be fooled"
    - "generating sentences by editing prototypes"
    - "learning to paraphrase for question answering"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "controlling linguistic style aspects in neural language generation"
    - "learning paraphrastic sentence embeddings from back translated bitext"
    - "steering output style and topic in neural response generation"
    - "a deep generative framework for paraphrase generation"

Connected through hyperedge HE-8 (induced by source paper "awe asymmetric word embedding for textual entailment"):
  with members:
    - "end task oriented textual entailment via deep explorations of inter sentence interactions"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "natural language inference over interaction space"
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "attention is all you need"
    - "learned in translation contextualized word vectors"
    - "specialising word vectors for lexical entailment"
    - "bilateral multi perspective matching for natural language sentences"

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)