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: "hierarchical question answering for long documents"
  Abstract: We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models. While most successful approaches for reading comprehension rely on recurrent neural networks (RNNs), running them over long documents is prohibitively slow because it is difficult to parallelize over sequences. Inspired by how people first skim the document, identify relevant parts, and carefully read these parts to produce an answer, we combine a coarse, fast model for selecting relevant sentences and a more expensive RNN for producing the answer from those sentences. We treat sentence selection as a latent variable trained jointly from the answer only using reinforcement learning. Experiments demonstrate the state of the art performance on a challenging subset of the Wikireading and on a new dataset, while speeding up the model by 3.5x-6.7x.

Connected through hyperedge HE-1 (induced by source paper "clicr a dataset of clinical case reports for machine reading comprehension"):
  with members:
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "quasar datasets for question answering by search and reading"
    - "question answering from unstructured text by retrieval and comprehension"
    - "constructing datasets for multi hop reading comprehension across documents"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "wikireading a novel large scale language understanding task over wikipedia"
    - "text understanding with the attention sum reader network"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-2 (induced by source paper "learning to skim text"):
  with members:
    - "learning recurrent span representations for extractive question answering"
    - "reasonet learning to stop reading in machine comprehension"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "a parallel hierarchical model for machine comprehension on sparse data"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "adaptive computation time for recurrent neural networks"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-3 (induced by source paper "question answering on knowledge bases and text using universal schema and memory networks"):
  with members:
    - "learning recurrent span representations for extractive question answering"
    - "newsqa a machine comprehension dataset"
    - "squad 100 000 questions for machine comprehension of text"
    - "key value memory networks for directly reading documents"
    - "ms marco a human generated machine reading comprehension dataset"
    - "learning to compose neural networks for question answering"
    - "dynamic coattention networks for question answering"
    - "machine comprehension using match lstm and answer pointer"

Connected through hyperedge HE-4 (induced by source paper "multi label classification of patient notes a case study on icd code assignment"):
  with members:
    - "fasttext zip compressing text classification models"
    - "structured prediction models for rnn based sequence labeling in clinical text"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "learning to diagnose with lstm recurrent neural networks"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "efficient estimation of word representations in vector space"

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)