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: "joint training of candidate extraction and answer selection for reading comprehension"
  Abstract: While sophisticated neural-based techniques have been developed in reading comprehension, most approaches model the answer in an independent manner, ignoring its relations with other answer candidates. This problem can be even worse in open-domain scenarios, where candidates from multiple passages should be combined to answer a single question. In this paper, we formulate reading comprehension as an extract-then-select two-stage procedure. We first extract answer candidates from passages, then select the final answer by combining information from all the candidates. Furthermore, we regard candidate extraction as a latent variable and train the two-stage process jointly with reinforcement learning. As a result, our approach has improved the state-of-the-art performance significantly on two challenging open-domain reading comprehension datasets. Further analysis demonstrates the effectiveness of our model components, especially the information fusion of all the candidates and the joint training of the extract-then-select procedure.

Connected through hyperedge HE-1 (induced by source paper "a multi type multi span network for reading comprehension that requires discrete reasoning"):
  with members:
    - "retrieve read rerank towards end to end multi document reading comprehension"
    - "analysing mathematical reasoning abilities of neural models"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "product aware answer generation in e commerce question answering"):
  with members:
    - "learning to remember translation history with a continuous cache"
    - "ruber an unsupervised method for automatic evaluation of open domain dialog systems"
    - "an abstractive approach to question answering"
    - "query based abstractive summarization using neural networks"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"
    - "get to the point summarization with pointer generator networks"
    - "improved training of wasserstein gans"
    - "s net from answer extraction to answer generation for machine reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "joint learning of answer selection and answer summary generation in community question answering"):
  with members:
    - "semeval 2017 task 3 community question answering"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "neural document summarization by jointly learning to score and select sentences"
    - "wikihow a large scale text summarization dataset"
    - "joint multitask learning for community question answering using task specific embeddings"
    - "multi task learning with multi view attention for answer selection and knowledge base question answering"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"

Connected through hyperedge HE-4 (induced by source paper "neural arabic question answering"):
  with members:
    - "semeval 2017 task 3 community question answering"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention is all you need"
    - "fasttext zip compressing text classification models"
    - "newsqa a machine comprehension dataset"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-5 (induced by source paper "exploring graph structured passage representation for multi hop reading comprehension with graph neural networks"):
  with members:
    - "the web as a knowledge base for answering complex questions"
    - "n ary relation extraction using graph state lstm"
    - "neural models for reasoning over multiple mentions using coreference"
    - "deep contextualized word representations"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "a graph to sequence model for amr to text generation"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "searchqa a new q a dataset augmented with context from a search engine"

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)