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: "selqa a new benchmark for selection based question answering"
  Abstract: This paper presents a new selection-based question answering dataset, SelQA. The dataset consists of questions generated through crowdsourcing and sentence length answers that are drawn from the ten most prevalent topics in the English Wikipedia. We introduce a corpus annotation scheme that enhances the generation of large, diverse, and challenging datasets by explicitly aiming to reduce word co-occurrences between the question and answers. Our annotation scheme is composed of a series of crowdsourcing tasks with a view to more effectively utilize crowdsourcing in the creation of question answering datasets in various domains. Several systems are compared on the tasks of answer sentence selection and answer triggering, providing strong baseline results for future work to improve upon.

Connected through hyperedge HE-1 (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-2 (induced by source paper "sanity check a strong alignment and information retrieval baseline for question answering"):
  with members:
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "bridging the gap incorporating a semantic similarity measure for effectively mapping pubmed queries to documents"
    - "improved answer selection with pre trained word embeddings"
    - "recent trends in deep learning based natural language processing"
    - "answering complex questions using open information extraction"
    - "attentive pooling networks"
    - "a decomposable attention model for natural language inference"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-3 (induced by source paper "combining graph based dependency features with convolutional neural network for answer triggering"):
  with members:
    - "attention based convolutional neural network for machine comprehension"
    - "dynamic memory networks for visual and textual question answering"
    - "neural variational inference for text processing"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "boosting named entity recognition with neural character embeddings"
    - "a deep architecture for semantic matching with multiple positional sentence representations"
    - "deep learning for answer sentence selection"
    - "a convolutional neural network for modelling sentences"

Connected through hyperedge HE-4 (induced by source paper "adaptive convolutional filter generation for natural language understanding"):
  with members:
    - "multi view sentence representation learning"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "joint embedding of words and labels for text classification"
    - "diffusion maps for textual network embedding"
    - "topic compositional neural language model"
    - "deconvolutional latent variable model for text sequence matching"
    - "dynamic compositional neural networks over tree structure"
    - "learning loss functions for semi supervised learning via discriminative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "learning loss functions for semi supervised learning via discriminative adversarial networks"):
  with members:
    - "improved semi supervised learning with gans using manifold invariances"
    - "attention over attention neural networks for reading comprehension"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "bilateral multi perspective matching for natural language sentences"
    - "attentive pooling networks"
    - "improved techniques for training gans"
    - "iterative alternating neural attention for machine reading"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "classification of radiology reports using neural attention models"):
  with members:
    - "lexicon integrated cnn models with attention for sentiment analysis"
    - "neural architectures for named entity recognition"
    - "where to look focus regions for visual question answering"
    - "recurrent models of visual attention"
    - "deep networks with internal selective attention through feedback connections"
    - "convolutional neural networks for sentence classification"
    - "deformable part models are convolutional neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-7 (induced by source paper "milkqa a dataset of consumer questions for the task of answer selection"):
  with members:
    - "sentence similarity learning by lexical decomposition and composition"
    - "squad 100 000 questions for machine comprehension of text"
    - "ms marco a human generated machine reading comprehension dataset"
    - "semi supervised question retrieval with gated convolutions"
    - "applying deep learning to answer selection a study and an open task"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "iss mult intelligent sample selection for multi task learning in question answering"):
  with members:
    - "implicit discourse relation classification via multi task neural networks"
    - "squad 100 000 questions for machine comprehension of text"
    - "how transferable are features in deep neural networks"
    - "deep learning for answer sentence selection"
    - "distributed representations of words and phrases and their compositionality"

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)