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: "diversity driven attention model for query based abstractive summarization"
  Abstract: Abstractive summarization aims to generate a shorter version of the document covering all the salient points in a compact and coherent fashion. On the other hand, query-based summarization highlights those points that are relevant in the context of a given query. The encode-attend-decode paradigm has achieved notable success in machine translation, extractive summarization, dialog systems, etc. But it suffers from the drawback of generation of repeated phrases. In this work we propose a model for the query-based summarization task based on the encode-attend-decode paradigm with two key additions (i) a query attention model (in addition to document attention model) which learns to focus on different portions of the query at different time steps (instead of using a static representation for the query) and (ii) a new diversity based attention model which aims to alleviate the problem of repeating phrases in the summary. In order to enable the testing of this model we introduce a new query-based summarization dataset building on debatepedia. Our experiments show that with these two additions the proposed model clearly outperforms vanilla encode-attend-decode models with a gain of 28\% (absolute) in ROUGE-L scores.

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"
    - "query based abstractive summarization using neural networks"
    - "learning paraphrastic sentence embeddings from back translated bitext"

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 "select and attend towards controllable content selection in text generation"):
  with members:
    - "toward unsupervised text content manipulation"
    - "towards content transfer through grounded text generation"
    - "step by step separating planning from realization in neural data to text generation"
    - "global encoding for abstractive summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "bottom up abstractive summarization"
    - "show control and tell a framework for generating controllable and grounded captions"

Connected through hyperedge HE-5 (induced by source paper "generating diverse translation by manipulating multi head attention"):
  with members:
    - "information aggregation for multi head attention with routing by agreement"
    - "mixture models for diverse machine translation tricks of the trade"
    - "investigating backtranslation in neural machine translation"
    - "d page diverse paraphrase generation"
    - "multi head attention with disagreement regularization"
    - "convolutional self attention network"
    - "sequence to sequence mixture model for diverse machine translation"
    - "generating more interesting responses in neural conversation models with distributional constraints"

Connected through hyperedge HE-6 (induced by source paper "conditionally learn to pay attention for sequential visual task"):
  with members:
    - "learn to pay attention"
    - "a unified query based generative model for question generation and question answering"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "image captioning with semantic attention"
    - "hierarchical attention networks"
    - "progressive attention networks for visual attribute prediction"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-7 (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"
    - "joint training of candidate extraction and answer selection for reading comprehension"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "deep reinforcement learning with distributional semantic rewards for abstractive summarization"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "beyond bleu training neural machine translation with semantic similarity"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "get to the point summarization with pointer generator networks"
    - "a deep reinforced model for abstractive summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "neural combinatorial optimization with reinforcement learning"

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)