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: "applying deep learning to answer selection a study and an open task"
  Abstract: We apply a general deep learning framework to address the non-factoid question answering task. Our approach does not rely on any linguistic tools and can be applied to different languages or domains. Various architectures are presented and compared. We create and release a QA corpus and setup a new QA task in the insurance domain. Experimental results demonstrate superior performance compared to the baseline methods and various technologies give further improvements. For this highly challenging task, the top-1 accuracy can reach up to 65.3% on a test set, which indicates a great potential for practical use.

Connected through hyperedge HE-1 (induced by source paper "hybrid tiled convolutional neural networks for text sentiment classification"):
  with members:
    - "parameterized convolutional neural networks for aspect level sentiment classification"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "constructing a word similarity graph from vector based word representation for named entity recognition"
    - "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"
    - "comparative study of cnn and rnn for natural language processing"
    - "exploring different dimensions of attention for uncertainty detection"
    - "lexicon integrated cnn models with attention for sentiment analysis"
    - "learning text representation using recurrent convolutional neural network with highway layers"

Connected through hyperedge HE-2 (induced by source paper "a deep look into neural ranking models for information retrieval"):
  with members:
    - "learning groupwise scoring functions using deep neural networks"
    - "simple applications of bert for ad hoc document retrieval"
    - "semeval 2017 task 3 community question answering"
    - "user intent prediction in information seeking conversations"
    - "vitor learning to rank webpages based on visual features"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "cross domain regularization for neural ranking models using adversarial learning"

Connected through hyperedge HE-3 (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"
    - "learning to paraphrase for question answering"
    - "sentence simplification with deep reinforcement learning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "machine reading comprehension for answer re ranking in customer support chatbots"):
  with members:
    - "semeval 2017 task 3 community question answering"
    - "towards automated customer support"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "attention is all you need"
    - "towards an automatic turing test learning to evaluate dialogue responses"

Connected through hyperedge HE-6 (induced by source paper "privacy and utility preserving textual analysis via calibrated multivariate perturbations"):
  with members:
    - "auditing data provenance in text generation models"
    - "the natural auditor how to tell if someone used your words to train their model"
    - "technical privacy metrics a systematic survey"
    - "syntf synthetic and differentially private term frequency vectors for privacy preserving text mining"
    - "plausible deniability for privacy preserving data synthesis"
    - "enriching word vectors with subword information"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-7 (induced by source paper "question answering for privacy policies combining computational and legal perspectives"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"
    - "polisis automated analysis and presentation of privacy policies using deep learning"
    - "quac question answering in context"
    - "stress test evaluation for natural language inference"

Connected through hyperedge HE-8 (induced by source paper "gated group self attention for answer selection"):
  with members:
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "self attentional acoustic models"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "non local neural networks"
    - "attention is all you need"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "deep semantic role labeling with self attention"
    - "convolutional sequence to sequence learning"

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)