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: "query based abstractive summarization using neural networks"
  Abstract: Creating short summaries of documents with respect to a query has applications in for#N#example search engines, where it may help inform users of the most relevant results.#N#Constructing such a summary automatically, with the potential expressiveness of a#N#human-written summary, is a difficult problem yet to be fully solved. In this thesis,#N#a neural network model for this task is presented. We adapt an existing dataset of#N#news article summaries for the task and train a pointer-generator model using this#N#dataset to summarize such articles. The generated summaries are then evaluated#N#by measuring similarity to reference summaries. We observe that the generated#N#summaries exhibit abstractive properties, but also that they have issues, such as#N#rarely being truthful. However, we show that a neural network summarization model,#N#similar to existing neural network models for abstractive summarization, can be#N#constructed to make use of queries for more targeted summaries.

Connected through hyperedge HE-1 (induced by source paper "conditional self attention for query based summarization"):
  with members:
    - "universal transformers"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "generating wikipedia by summarizing long sequences"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "enhancing sentence embedding with generalized pooling"
    - "reinforced mnemonic reader for machine comprehension"

Connected through hyperedge HE-2 (induced by source paper "abstractive snippet generation"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "generating wikipedia by summarizing long sequences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "opennmt open source toolkit for neural machine translation"
    - "learning paraphrastic sentence embeddings from back translated bitext"
    - "diversity driven attention model for query based abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "multi style generative reading comprehension"):
  with members:
    - "learning and evaluating general linguistic intelligence"
    - "a deep cascade model for multi document reading comprehension"
    - "densely connected attention propagation for reading comprehension"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "the natural language decathlon multitask learning as question answering"
    - "bottom up abstractive summarization"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-4 (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"
    - "an abstractive approach to question answering"
    - "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"

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)