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 paraphrase identification of questions with noisy pretraining"
  Abstract: We present a solution to the problem of paraphrase identification of questions. We focus on a recent dataset of question pairs annotated with binary paraphrase labels and show that a variant of the decomposable attention model (Parikh et al., 2016) results in accurate performance on this task, while being far simpler than many competing neural architectures. Furthermore, when the model is pretrained on a noisy dataset of automatically collected question paraphrases, it obtains the best reported performance on the dataset.

Connected through hyperedge HE-1 (induced by source paper "simple and effective text matching with richer alignment features"):
  with members:
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "stochastic answer networks for natural language inference"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "natural language inference over interaction space"
    - "bilateral multi perspective matching for natural language sentences"
    - "a decomposable attention model for natural language inference"
    - "enhanced lstm for natural language inference"
    - "key value memory networks for directly reading documents"

Connected through hyperedge HE-2 (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-3 (induced by source paper "conflict as an inverse of attention in sequence relationship"):
  with members:
    - "deep contextualized word representations"
    - "stochastic answer networks for natural language inference"
    - "attention is all you need"
    - "bilateral multi perspective matching for natural language sentences"
    - "deep ctr prediction in display advertising"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "query bag matching with mutual coverage for information seeking conversations in e commerce"):
  with members:
    - "simple and effective text matching with richer alignment features"
    - "transfer learning for context aware question matching in information seeking conversations in e commerce"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"
    - "enhanced lstm for natural language inference"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "learning general purpose distributed sentence representations via large scale multi task learning"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "learning to generate reviews and discovering sentiment"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "rethinking skip thought a neighborhood based approach"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "deconvolutional latent variable model for text sequence matching"
    - "dissent sentence representation learning from explicit discourse relations"
    - "learned in translation contextualized word vectors"

Connected through hyperedge HE-6 (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"

Connected through hyperedge HE-7 (induced by source paper "semantic sentence matching with densely connected recurrent and co attentive information"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "deep contextualized word representations"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "natural language inference over interaction space"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "adaptive convolutional filter generation for natural language understanding"

Connected through hyperedge HE-8 (induced by source paper "sentence encoding with tree constrained relation networks"):
  with members:
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "structured attention networks"
    - "learning structured text representations"
    - "attention is all you need"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"
    - "neural probabilistic model for non projective mst parsing"
    - "graph convolutional encoders for syntax aware neural machine translation"

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)