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: "an abstractive approach to question answering"
  Abstract: Question Answering has come a long way from answer sentence selection, relational QA to reading and comprehension. We move our attention to abstractive question answering by which we facilitate machine to read passages and answer questions by generating them. We frame the problem as a sequence to sequence learning where the encoder being a network that models the relation between question and passage, thereby relying solely on passage and question content to form an abstraction of the answer. Not being able to retain facts and making repetitions are common mistakes that affect the overall legibility of answers. To counter these issues, we employ copying mechanism and maintenance of coverage vector in our model respectively. Our results on MS-MARCO demonstrates it's superiority over baselines and we also show qualitative examples where we improved in terms of correctness and readability.

Connected through hyperedge HE-1 (induced by source paper "tydi qa a benchmark for information seeking question answering in typologically diverse languages"):
  with members:
    - "neural arabic question answering"
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "latent retrieval for weakly supervised open domain question answering"
    - "what question answering can learn from trivia nerds"
    - "question answering is a format when is it useful"
    - "korquad1 0 korean qa dataset for machine reading comprehension"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "mlqa evaluating cross lingual extractive question answering"

Connected through hyperedge HE-2 (induced by source paper "product aware answer generation in e commerce question answering"):
  with members:
    - "joint training of candidate extraction and answer selection for reading comprehension"
    - "learning to remember translation history with a continuous cache"
    - "ruber an unsupervised method for automatic evaluation of open domain dialog systems"
    - "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 "incorporating external knowledge into machine reading for generative question answering"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "improving question answering by commonsense based pre training"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "commonsense for generative multi hop question answering tasks"
    - "diversity driven attention model for query based abstractive summarization"
    - "get to the point summarization with pointer generator networks"
    - "s net from answer extraction to answer generation for machine reading comprehension"

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)