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: "attention based convolutional neural network for machine comprehension"
  Abstract: Understanding open-domain text is one of the primary challenges in natural language processing (NLP). Machine comprehension benchmarks evaluate the system's ability to understand text based on the text content only. In this work, we investigate machine comprehension on MCTest, a question answering (QA) benchmark. Prior work is mainly based on feature engineering approaches. We come up with a neural network framework, named hierarchical attention-based convolutional neural network (HABCNN), to address this task without any manually designed features. Specifically, we explore HABCNN for this task by two routes, one is through traditional joint modeling of passage, question and answer, one is through textual entailment. HABCNN employs an attention mechanism to detect key phrases, key sentences and key snippets that are relevant to answering the question. Experiments show that HABCNN outperforms prior deep learning approaches by a big margin.

Connected through hyperedge HE-1 (induced by source paper "reasoning driven question answering for natural language understanding"):
  with members:
    - "on the capabilities and limitations of reasoning for natural language understanding"
    - "evidence sentence extraction for machine reading comprehension"
    - "question answering as global reasoning over semantic abstractions"
    - "going on a vacation takes longer than going for a walk a study of temporal commonsense understanding"
    - "cogcomptime a tool for understanding time in natural language text"
    - "annotation artifacts in natural language inference data"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "multi hop knowledge graph reasoning with reward shaping"

Connected through hyperedge HE-2 (induced by source paper "option comparison network for multiple choice reading comprehension"):
  with members:
    - "eliminet a model for eliminating options for reading comprehension with multiple choice questions"
    - "deep contextualized word representations"
    - "multi range reasoning for machine comprehension"
    - "improving machine reading comprehension with general reading strategies"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "race large scale reading comprehension dataset from examinations"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-3 (induced by source paper "answering science exam questions using query rewriting with background knowledge"):
  with members:
    - "query expansion techniques for information retrieval a survey"
    - "improving question answering by commonsense based pre training"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "kg 2 learning to reason science exam questions with contextual knowledge graph embeddings"
    - "know what you don t know unanswerable questions for squad"
    - "end task oriented textual entailment via deep explorations of inter sentence interactions"
    - "improving natural language inference using external knowledge in the science questions domain"
    - "improving machine reading comprehension with general reading strategies"

Connected through hyperedge HE-4 (induced by source paper "a systematic classification of knowledge reasoning and context within the arc dataset"):
  with members:
    - "question answering as global reasoning over semantic abstractions"
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "learning recurrent span representations for extractive question answering"
    - "crowdsourcing multiple choice science questions"
    - "reading wikipedia to answer open domain questions"
    - "simple and effective multi paragraph reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "a co matching model for multi choice reading comprehension"):
  with members:
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "race large scale reading comprehension dataset from examinations"
    - "dynamic fusion networks for machine reading comprehension"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "squad 100 000 questions for machine comprehension of text"
    - "ms marco a human generated machine reading comprehension dataset"
    - "a compare aggregate model for matching text sequences"
    - "gated attention readers for text comprehension"

Connected through hyperedge HE-6 (induced by source paper "caesar context awareness enabled summary attentive reader"):
  with members:
    - "end to end answer chunk extraction and ranking for reading comprehension"
    - "learning recurrent span representations for extractive question answering"
    - "reasonet learning to stop reading in machine comprehension"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "learning a natural language interface with neural programmer"
    - "dynamic coattention networks for question answering"
    - "multi perspective context matching for machine comprehension"

Connected through hyperedge HE-7 (induced by source paper "combining graph based dependency features with convolutional neural network for answer triggering"):
  with members:
    - "dynamic memory networks for visual and textual question answering"
    - "selqa a new benchmark for selection based 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-8 (induced by source paper "generating synthetic data for neural keyword to question models"):
  with members:
    - "beam search strategies for neural machine translation"
    - "learning classifiers from synthetic data using a multichannel autoencoder"
    - "scenenet understanding real world indoor scenes with synthetic data"
    - "adam a method for stochastic optimization"
    - "recurrent models of visual attention"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

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)