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: "reasoning with memory augmented neural networks for language comprehension"
  Abstract: Hypothesis testing is an important cognitive process that supports human reasoning. In this paper, we introduce a computational hypothesis testing approach based on memory augmented neural networks. Our approach involves a hypothesis testing loop that reconsiders and progressively refines a previously formed hypothesis in order to generate new hypotheses to test. We apply the proposed approach to language comprehension task by using Neural Semantic Encoders (NSE). Our NSE models achieve the state-of-the-art results showing an absolute improvement of 1.2% to 2.6% accuracy over previous results obtained by single and ensemble systems on standard machine comprehension benchmarks such as the Children's Book Test (CBT) and Who-Did-What (WDW) news article datasets.

Connected through hyperedge HE-1 (induced by source paper "dynamically fused graph network for multi hop reasoning"):
  with members:
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "the web as a knowledge base for answering complex questions"
    - "neural models for reasoning over multiple mentions using coreference"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "exploring graph structured passage representation for multi hop reading comprehension with graph neural networks"
    - "stochastic answer networks for machine reading comprehension"
    - "constructing datasets for multi hop reading comprehension across documents"

Connected through hyperedge HE-2 (induced by source paper "contextual recurrent units for cloze style reading comprehension"):
  with members:
    - "dataset and neural recurrent sequence labeling model for open domain factoid question answering"
    - "text understanding with the attention sum reader network"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "iterative alternating neural attention for machine reading"
    - "consensus attention based neural networks for chinese reading comprehension"
    - "words or characters fine grained gating for reading comprehension"
    - "ac blstm asymmetric convolutional bidirectional lstm networks for text classification"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "effective character augmented word embedding for machine reading comprehension"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "lingke a fine grained multi turn chatbot for customer service"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "subword augmented embedding for cloze reading comprehension"):
  with members:
    - "deep enhanced representation for implicit discourse relation recognition"
    - "deep contextualized word representations"
    - "one shot learning for question answering in gaokao history challenge"
    - "sjtu nlp at semeval 2018 task 9 neural hypernym discovery with term embeddings"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "character word lstm language models"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "fast and accurate neural word segmentation for chinese"

Connected through hyperedge HE-5 (induced by source paper "a boo n for evaluating architecture performance"):
  with members:
    - "deep reinforcement learning that matters"
    - "attention over attention neural networks for reading comprehension"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "densely connected convolutional networks"
    - "dynamic coattention networks for question answering"
    - "natural language comprehension with the epireader"
    - "gated attention readers for text comprehension"

Connected through hyperedge HE-6 (induced by source paper "linguistic knowledge as memory for recurrent neural networks"):
  with members:
    - "frustratingly short attention spans in neural language modeling"
    - "a neural knowledge language model"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "broad context language modeling as reading comprehension"
    - "gated graph sequence neural networks"
    - "text understanding with the attention sum reader network"
    - "squad 100 000 questions for machine comprehension of text"
    - "the lambada dataset word prediction requiring a broad discourse context"

Connected through hyperedge HE-7 (induced by source paper "neural skill transfer from supervised language tasks to reading comprehension"):
  with members:
    - "semanticz at semeval 2016 task 3 ranking relevant answers in community question answering using semantic similarity based on fine tuned word embeddings"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "dissent sentence representation learning from explicit discourse relations"
    - "making neural qa as simple as possible but not simpler"
    - "reasonet learning to stop reading in machine comprehension"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "relnet end to end modeling of entities relations"):
  with members:
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "a simple neural network module for relational reasoning"
    - "reading wikipedia to answer open domain questions"
    - "gated graph sequence neural networks"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "tracking the world state with recurrent entity networks"
    - "key value memory networks for directly reading documents"
    - "learning a natural language interface with neural programmer"

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)