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: "entity commonsense representation for neural abstractive summarization"
  Abstract: A major proportion of a text summary includes important entities found in the original text. These entities build up the topic of the summary. Moreover, they hold commonsense information once they are linked to a knowledge base. Based on these observations, this paper investigates the usage of linked entities to guide the decoder of a neural text summarizer to generate concise and better summaries. To this end, we leverage on an off-the-shelf entity linking system (ELS) to extract linked entities and propose Entity2Topic (E2T), a module easily attachable to a sequence-to-sequence model that transforms a list of entities into a vector representation of the topic of the summary. Current available ELS's are still not sufficiently effective, possibly introducing unresolved ambiguities and irrelevant entities. We resolve the imperfections of the ELS by (a) encoding entities with selective disambiguation, and (b) pooling entity vectors using firm attention. By applying E2T to a simple sequence-to-sequence model with attention mechanism as base model, we see significant improvements of the performance in the Gigaword (sentence to title) and CNN (long document to multi-sentence highlights) summarization datasets by at least 2 ROUGE points.

Connected through hyperedge HE-1 (induced by source paper "distilling the knowledge of bert for text generation"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "towards making the most of bert in neural machine translation"
    - "mask predict parallel decoding of conditional masked language models"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"
    - "pretraining based natural language generation for text summarization"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "patient knowledge distillation for bert model compression"

Connected through hyperedge HE-2 (induced by source paper "multi stage pretraining for abstractive summarization"):
  with members:
    - "the curious case of neural text degeneration"
    - "pretraining based natural language generation for text summarization"
    - "neural text generation with unlikelihood training"
    - "leveraging pre trained checkpoints for sequence generation tasks"
    - "rethinking imagenet pre training"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "actor critic based training framework for abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "an entity driven framework for abstractive summarization"):
  with members:
    - "discofuse a large scale dataset for discourse based sentence fusion"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "learning to extract coherent summary via deep reinforcement learning"
    - "robust neural abstractive summarization systems and evaluation against adversarial information"

Connected through hyperedge HE-4 (induced by source paper "highres highlight based reference less evaluation of summarization"):
  with members:
    - "jointly extracting and compressing documents with summary state representations"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "content selection in deep learning models of summarization"
    - "global encoding for abstractive summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "ranking sentences for extractive summarization with reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "improving neural question generation using answer separation"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "query and output generating words by querying distributed word representations for paraphrase generation"
    - "learning to ask neural question generation for reading comprehension"
    - "neural question generation from text a preliminary study"
    - "question answering and question generation as dual tasks"
    - "bilateral multi perspective matching for natural language sentences"
    - "pointing the unknown words"
    - "abstractive text summarization using sequence to sequence rnns and beyond"

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)