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: "neural network models for paraphrase identification semantic textual similarity natural language inference and question answering"
  Abstract: In this paper, we analyze several neural network designs (and their variations) for sentence pair modeling and compare their performance extensively across eight datasets, including paraphrase identification, semantic textual similarity, natural language inference, and question answering tasks. Although most of these models have claimed state-of-the-art performance, the original papers often reported on only one or two selected datasets. We provide a systematic study and show that (i) encoding contextual information by LSTM and inter-sentence interactions are critical, (ii) Tree-LSTM does not help as much as previously claimed but surprisingly improves performance on Twitter datasets, (iii) the Enhanced Sequential Inference Model is the best so far for larger datasets, while the Pairwise Word Interaction Model achieves the best performance when less data is available. We release our implementations as an open-source toolkit.

Connected through hyperedge HE-1 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks"):
  with members:
    - "attentive mimicking better word embeddings by attending to informative contexts"
    - "phonology augmented statistical framework for machine transliteration using limited linguistic resources"
    - "chinese japanese unsupervised neural machine translation using sub character level information"
    - "neural machine translation of logographic languages using sub character level information"
    - "generalizing word embeddings using bag of subwords"
    - "dual long short term memory networks for sub character representation learning"
    - "extrapolation in nlp"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "pre training for query rewriting in a spoken language understanding system"):
  with members:
    - "knowledge distillation from internal representations"
    - "training neural response selection for task oriented dialogue systems"
    - "feedback based self learning in large scale conversational ai agents"
    - "learning from past mistakes improving automatic speech recognition output via noisy clean phrase context modeling"
    - "towards end to end spoken language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"

Connected through hyperedge HE-3 (induced by source paper "generalizing natural language analysis through span relation representations"):
  with members:
    - "enriching pre trained language model with entity information for relation classification"
    - "bert rediscovers the classical nlp pipeline"
    - "spanbert improving pre training by representing and predicting spans"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "multi task deep neural networks for natural language understanding"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "simple bert models for relation extraction and semantic role labeling"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"

Connected through hyperedge HE-4 (induced by source paper "coacor code annotation for code retrieval with reinforcement learning"):
  with members:
    - "comment generation for source code state of the art challenges and opportunities"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "visual question generation as dual task of visual question answering"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "a survey of machine learning for big code and naturalness"

Connected through hyperedge HE-5 (induced by source paper "paws paraphrase adversaries from word scrambling"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "assessing composition in sentence vector representations"
    - "gender bias in coreference resolution"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "generating natural language adversarial examples"
    - "open subtitles paraphrase corpus for six languages"

Connected through hyperedge HE-6 (induced by source paper "multi task sentence encoding model for semantic retrieval in question answering systems"):
  with members:
    - "natural language inference over interaction space"
    - "attention is all you need"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "a structured self attentive sentence embedding"
    - "bilateral multi perspective matching for natural language sentences"
    - "learning to compose task specific tree structures"
    - "disan directional self attention network for rnn cnn free language understanding"

Connected through hyperedge HE-7 (induced by source paper "generating token level explanations for natural language inference"):
  with members:
    - "attention is not explanation"
    - "pair2vec compositional word pair embeddings for cross sentence inference"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "e snli natural language inference with natural language explanations"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-8 (induced by source paper "a multi cascaded model with data augmentation for enhanced paraphrase detection in short texts"):
  with members:
    - "a multi cascaded deep model for bilingual sms classification"
    - "a deep network model for paraphrase detection in short text messages"
    - "deep contextualized word representations"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "neural paraphrase identification of questions with noisy pretraining"

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)