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 similarity learning by lexical decomposition and composition"
  Abstract: Most conventional sentence similarity methods only focus on similar parts of two input sentences, and simply ignore the dissimilar parts, which usually give us some clues and semantic meanings about the sentences. In this work, we propose a model to take into account both the similarities and dissimilarities by decomposing and composing lexical semantics over sentences. The model represents each word as a vector, and calculates a semantic matching vector for each word based on all words in the other sentence. Then, each word vector is decomposed into a similar component and a dissimilar component based on the semantic matching vector. After this, a two-channel CNN model is employed to capture features by composing the similar and dissimilar components. Finally, a similarity score is estimated over the composed feature vectors. Experimental results show that our model gets the state-of-the-art performance on the answer sentence selection task, and achieves a comparable result on the paraphrase identification task.

Connected through hyperedge HE-1 (induced by source paper "banditrank learning to rank using contextual bandits"):
  with members:
    - "reinforcement learning for online information seeking"
    - "banditsum extractive summarization as a contextual bandit"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi cast attention networks for retrieval based question answering and response prediction"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "bilateral multi perspective matching for natural language sentences"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-2 (induced by source paper "selection bias explorations and debias methods for natural language sentence matching datasets"):
  with members:
    - "sentence modeling via multiple word embeddings and multi level comparison for semantic textual similarity"
    - "annotation artifacts in natural language inference data"
    - "what makes reading comprehension questions easier"
    - "hypothesis only baselines in natural language inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "natural language inference over interaction space"

Connected through hyperedge HE-3 (induced by source paper "dolphin a verbal fluency evaluation system for elementary education"):
  with members:
    - "automatic short answer grading via multiway attention networks"
    - "a multimodal alerting system for online class quality assurance"
    - "learning effective embeddings from crowdsourced labels an educational case study"
    - "disfluency detection using a noisy channel model and a deep neural language model"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "learning to compare relation network for few shot learning"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-4 (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"
    - "neural network models for paraphrase identification semantic textual similarity natural language inference and question answering"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "dolphin a spoken language proficiency assessment system for elementary education"):
  with members:
    - "automatic short answer grading via multiway attention networks"
    - "a multimodal alerting system for online class quality assurance"
    - "learning effective embeddings from crowdsourced labels an educational case study"
    - "disfluency detection using a noisy channel model and a deep neural language model"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "learning to compare relation network for few shot learning"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-6 (induced by source paper "measuring sentences similarity a survey"):
  with members:
    - "calculating the similarity between words and sentences using a lexical database and corpus statistics"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "learning review representations from user and product level information for spam detection"):
  with members:
    - "adadelta an adaptive learning rate method"
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-8 (induced by source paper "co stack residual affinity networks with multi level attention refinement for matching text sequences"):
  with members:
    - "multi perspective relevance matching with hierarchical convnets for social media search"
    - "deep contextualized word representations"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "a continuously growing dataset of sentential paraphrases"
    - "natural language inference over interaction space"
    - "learned in translation contextualized word vectors"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "compare compress and propagate enhancing neural architectures with alignment factorization for natural language inference"

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)