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: "learning to paraphrase for question answering"
  Abstract: Question answering (QA) systems are sensitive to the many different ways natural language expresses the same information need. In this paper we turn to paraphrases as a means of capturing this knowledge and present a general framework which learns felicitous paraphrases for various QA tasks. Our method is trained end-to-end using question-answer pairs as a supervision signal. A question and its paraphrases serve as input to a neural scoring model which assigns higher weights to linguistic expressions most likely to yield correct answers. We evaluate our approach on QA over Freebase and answer sentence selection. Experimental results on three datasets show that our framework consistently improves performance, achieving competitive results despite the use of simple QA models.

Connected through hyperedge HE-1 (induced by source paper "paraphrase generation with latent bag of words"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "topic guided variational autoencoders for text generation"
    - "step by step separating planning from realization in neural data to text generation"
    - "a tutorial on deep latent variable models of natural language"
    - "end to end content and plan selection for data to text generation"
    - "learning neural templates for text generation"
    - "no metrics are perfect adversarial reward learning for visual storytelling"

Connected through hyperedge HE-2 (induced by source paper "automatic compilation of resources for academic writing and evaluating with informal word identification and paraphrasing system"):
  with members:
    - "tf ranking scalable tensorflow library for learning to rank"
    - "a report on the complex word identification shared task 2018"
    - "par4sim adaptive paraphrasing for text simplification"
    - "simple unsupervised keyphrase extraction using sentence embeddings"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wronging a right generating better errors to improve grammatical error detection"
    - "dynamic multi level multi task learning for sentence simplification"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-3 (induced by source paper "addressing semantic drift in question generation for semi supervised question answering"):
  with members:
    - "synthetic qa corpora generation with roundtrip consistency"
    - "learning to generate questions by learningwhat not to generate"
    - "evaluating rewards for question generation models"
    - "unified language model pre training for natural language understanding and generation"
    - "learning to navigate unseen environments back translation with environmental dropout"
    - "towards a better metric for evaluating question generation systems"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-4 (induced by source paper "controllable paraphrase generation with a syntactic exemplar"):
  with members:
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "toward unsupervised text content manipulation"
    - "text generation with exemplar based adaptive decoding"
    - "smaller text classifiers with discriminative cluster embeddings"
    - "variational sequential labelers for semi supervised learning"
    - "towards generating long and coherent text with multi level latent variable models"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "disentangled representation learning for non parallel text style transfer"

Connected through hyperedge HE-5 (induced by source paper "sequence to sequence pre training with data augmentation for sentence rewriting"):
  with members:
    - "corpora generation for grammatical error correction"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "investigating backtranslation in neural machine translation"
    - "multi task neural models for translating between styles within and across languages"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"

Connected through hyperedge HE-6 (induced by source paper "generative question refinement with deep reinforcement learning in retrieval based qa system"):
  with members:
    - "learning to coordinate multiple reinforcement learning agents for diverse query reformulation"
    - "proximal policy optimization and its dynamic version for sequence generation"
    - "identifying well formed natural language questions"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "sentence simplification with deep reinforcement learning"
    - "ask the right questions active question reformulation with reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "an empirical comparison on imitation learning and reinforcement learning for paraphrase generation"):
  with members:
    - "deep reinforcement learning for sequence to sequence models"
    - "learning semantic sentence embeddings using pair wise discriminator"
    - "the natural language decathlon multitask learning as question answering"
    - "a study of reinforcement learning for neural machine translation"
    - "deep reinforcement learning for chinese zero pronoun resolution"
    - "learning semantic sentence embeddings using sequential pair wise discriminator"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "a continuously growing dataset of sentential paraphrases"

Connected through hyperedge HE-8 (induced by source paper "look before you hop conversational question answering over knowledge graphs using judicious context expansion"):
  with members:
    - "answering complex questions by joining multi document evidence with quasi knowledge graphs"
    - "interpretation of natural language rules in conversational machine reading"
    - "comqa a community sourced dataset for complex factoid question answering with paraphrase clusters"
    - "the web as a knowledge base for answering complex questions"
    - "coqa a conversational question answering challenge"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "neural architecture for question answering using a knowledge graph and web corpus"

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)