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: "a parallel hierarchical model for machine comprehension on sparse data"
  Abstract: Understanding unstructured text is a major goal within natural language processing. Comprehension tests pose questions based on short text passages to evaluate such understanding. In this work, we investigate machine comprehension on the challenging {\it MCTest} benchmark. Partly because of its limited size, prior work on {\it MCTest} has focused mainly on engineering better features. We tackle the dataset with a neural approach, harnessing simple neural networks arranged in a parallel hierarchy. The parallel hierarchy enables our model to compare the passage, question, and answer from a variety of trainable perspectives, as opposed to using a manually designed, rigid feature set. Perspectives range from the word level to sentence fragments to sequences of sentences; the networks operate only on word-embedding representations of text. When trained with a methodology designed to help cope with limited training data, our Parallel-Hierarchical model sets a new state of the art for {\it MCTest}, outperforming previous feature-engineered approaches slightly and previous neural approaches by a significant margin (over 15\% absolute).

Connected through hyperedge HE-1 (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"
    - "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"
    - "globally normalized transition based neural networks"

Connected through hyperedge HE-2 (induced by source paper "end to end answer chunk extraction and ranking for reading comprehension"):
  with members:
    - "attention over attention neural networks for reading comprehension"
    - "attentive pooling networks"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "attention based convolutional neural network for machine comprehension"
    - "text understanding with the attention sum reader network"
    - "squad 100 000 questions for machine comprehension of text"
    - "iterative alternating neural attention for machine reading"
    - "consensus attention based neural networks for chinese reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "globally normalized reader"):
  with members:
    - "learning recurrent span representations for extractive question answering"
    - "neural question generation from text a preliminary study"
    - "making neural qa as simple as possible but not simpler"
    - "newsqa a machine comprehension dataset"
    - "sequence to sequence learning as beam search optimization"
    - "bidirectional attention flow for machine comprehension"
    - "distant supervision for relation extraction beyond the sentence boundary"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-4 (induced by source paper "natural language comprehension with the epireader"):
  with members:
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "text understanding with the attention sum reader network"
    - "modeling relational information in question answer pairs with convolutional neural networks"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "the goldilocks principle reading children s books with explicit memory representations"
    - "learning natural language inference with lstm"
    - "teaching machines to read and comprehend"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "a base camp for scaling ai"):
  with members:
    - "a persona based neural conversation model"
    - "harnessing deep neural networks with logic rules"
    - "learning executable semantic parsers for natural language understanding"
    - "squad 100 000 questions for machine comprehension of text"
    - "ice enabling non experts to build models interactively for large scale lopsided problems"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-6 (induced by source paper "machine comprehension based on learning to rank"):
  with members:
    - "teaching machines to read and comprehend"
    - "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)