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: "dataset and neural recurrent sequence labeling model for open domain factoid question answering"
  Abstract: While question answering (QA) with neural network, i.e. neural QA, has achieved promising results in recent years, lacking of large scale real-word QA dataset is still a challenge for developing and evaluating neural QA system. To alleviate this problem, we propose a large scale human annotated real-world QA dataset WebQA with more than 42k questions and 556k evidences. As existing neural QA methods resolve QA either as sequence generation or classification/ranking problem, they face challenges of expensive softmax computation, unseen answers handling or separate candidate answer generation component. In this work, we cast neural QA as a sequence labeling problem and propose an end-to-end sequence labeling model, which overcomes all the above challenges. Experimental results on WebQA show that our model outperforms the baselines significantly with an F1 score of 74.69% with word-based input, and the performance drops only 3.72 F1 points with more challenging character-based input.

Connected through hyperedge HE-1 (induced by source paper "investigating prior knowledge for challenging chinese machine reading comprehension"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "ernie enhanced representation through knowledge integration"
    - "geosqa a benchmark for scenario based question answering in the geography domain at high school level"
    - "pre training with whole word masking for chinese bert"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "chid a large scale chinese idiom dataset for cloze test"
    - "analogical reasoning on chinese morphological and semantic relations"
    - "exploiting sentence embedding for medical question answering"

Connected through hyperedge HE-2 (induced by source paper "cross lingual natural language generation via pre training"):
  with members:
    - "improved zero shot neural machine translation via ignoring spurious correlations"
    - "denoising based sequence to sequence pre training for text generation"
    - "spanbert improving pre training by representing and predicting spans"
    - "mass masked sequence to sequence pre training for language generation"
    - "ncls neural cross lingual summarization"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual training for automatic question generation"

Connected through hyperedge HE-3 (induced by source paper "sberquad russian reading comprehension dataset description and analysis"):
  with members:
    - "machine reading comprehension a literature review"
    - "beyond english only reading comprehension experiments in zero shot multilingual transfer for bulgarian"
    - "how multilingual is multilingual bert"
    - "adaptation of deep bidirectional multilingual transformers for russian language"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "on the cross lingual transferability of monolingual representations"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"

Connected through hyperedge HE-4 (induced by source paper "contextual recurrent units for cloze style reading comprehension"):
  with members:
    - "reasoning with memory augmented neural networks for language comprehension"
    - "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-5 (induced by source paper "joint training of candidate extraction and answer selection for reading comprehension"):
  with members:
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "reinforced mnemonic reader for machine comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "r 3 reinforced reader ranker for open domain question answering"
    - "quasar datasets for question answering by search and reading"
    - "learning recurrent span representations for extractive question answering"
    - "ask the right questions active question reformulation with reinforcement learning"
    - "reasonet learning to stop reading in machine comprehension"

Connected through hyperedge HE-6 (induced by source paper "medical exam question answering with large scale reading comprehension"):
  with members:
    - "attention over attention neural networks for reading 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"
    - "key value memory networks for directly reading documents"
    - "ms marco a human generated machine reading comprehension dataset"
    - "dynamic coattention networks for question answering"
    - "natural language comprehension with the epireader"

Connected through hyperedge HE-7 (induced by source paper "a3net adversarial and attention network for machine reading comprehension"):
  with members:
    - "simple recurrent units for highly parallelizable recurrence"
    - "stochastic answer networks for machine reading comprehension"
    - "making neural qa as simple as possible but not simpler"
    - "fusionnet fusing via fully aware attention with application to machine comprehension"
    - "reasonet learning to stop reading in machine comprehension"
    - "s net from answer extraction to answer generation for machine reading comprehension"
    - "reading wikipedia to answer open domain questions"
    - "adversarial examples for evaluating reading comprehension systems"

Connected through hyperedge HE-8 (induced by source paper "dependent gated reading for cloze style question answering"):
  with members:
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "tweet2vec character based distributed representations for social media"
    - "text understanding with the attention sum reader network"
    - "iterative alternating neural attention for machine reading"
    - "who did what a large scale person centered cloze dataset"
    - "natural language comprehension with the epireader"
    - "gated attention readers for text comprehension"
    - "finding function in form compositional character models for open vocabulary word representation"

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)