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: "separating answers from queries for neural reading comprehension"
  Abstract: We present a novel neural architecture for answering queries, designed to optimally leverage explicit support in the form of query-answer memories. Our model is able to refine and update a given query while separately accumulating evidence for predicting the answer. Its architecture reflects this separation with dedicated embedding matrices and loosely connected information pathways (modules) for updating the query and accumulating evidence. This separation of responsibilities effectively decouples the search for query related support and the prediction of the answer. On recent benchmark datasets for reading comprehension, our model achieves state-of-the-art results. A qualitative analysis demonstrates that the model effectively accumulates weighted evidence from the query and over multiple support retrieval cycles which results in a robust answer prediction.

Connected through hyperedge HE-1 (induced by source paper "why can t memory networks read effectively"):
  with members:
    - "understanding dataset design choices for multi hop reasoning"
    - "misleading failures of partial input baselines"
    - "blindfold baselines for embodied qa"
    - "compositional attention networks for machine reasoning"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "clicr a dataset of clinical case reports for machine reading comprehension"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"
    - "question answering on knowledge bases and text using universal schema and memory networks"

Connected through hyperedge HE-2 (induced by source paper "reasoning over rdf knowledge bases using deep learning"):
  with members:
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "ontology reasoning with deep neural networks"
    - "disentangling language and knowledge in task oriented dialogs"
    - "learning to compute word embeddings on the fly"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "end to end differentiable proving"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "neural symbolic learning and reasoning a survey and interpretation"

Connected through hyperedge HE-3 (induced by source paper "constructing datasets for multi hop reading comprehension across documents"):
  with members:
    - "cross sentence n ary relation extraction with graph lstms"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "neural question answering at bioasq 5b"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "end to end differentiable proving"
    - "zero shot relation extraction via reading comprehension"
    - "making neural qa as simple as possible but not simpler"

Connected through hyperedge HE-4 (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"
    - "a parallel hierarchical model for machine comprehension on sparse data"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "reasonet learning to stop reading in machine comprehension"):
  with members:
    - "attention over attention neural networks for reading comprehension"
    - "learning recurrent span representations for extractive question answering"
    - "exploring question understanding and adaptation in neural network based question answering"
    - "reading wikipedia to answer open domain questions"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "webnav a new large scale task for natural language based sequential decision making"
    - "bidirectional attention flow for machine comprehension"
    - "text understanding with the attention sum reader network"

Connected through hyperedge HE-6 (induced by source paper "frustratingly short attention spans in neural language modeling"):
  with members:
    - "attention over attention neural networks for reading comprehension"
    - "long short term memory networks for machine reading"
    - "higher order recurrent neural networks"
    - "text understanding with the attention sum reader network"
    - "consensus attention based neural networks for chinese reading comprehension"
    - "key value memory networks for directly reading documents"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "reference aware language models"

Connected through hyperedge HE-7 (induced by source paper "end to end differentiable proving"):
  with members:
    - "holstep a machine learning dataset for higher order logic theorem proving"
    - "reasonet learning to stop reading in machine comprehension"
    - "harnessing deep neural networks with logic rules"
    - "programming with a differentiable forth interpreter"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "lifted rule injection for relation embeddings"
    - "learning to compose neural networks for question answering"
    - "tensorlog a differentiable deductive database"

Connected through hyperedge HE-8 (induced by source paper "an empirical analysis of multiple turn reasoning strategies in reading comprehension tasks"):
  with members:
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "learning recurrent span representations for extractive question answering"
    - "making neural qa as simple as possible but not simpler"
    - "reasonet learning to stop reading in machine comprehension"
    - "exploring question understanding and adaptation in neural network based question answering"
    - "bidirectional attention flow for machine comprehension"
    - "iterative alternating neural attention for machine reading"
    - "ms marco a human generated machine reading comprehension dataset"

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)