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: "guided neural language generation for abstractive summarization using abstract meaning representation"
  Abstract: Recent work on abstractive summarization has made progress with neural encoder-decoder architectures. However, such models are often challenged due to their lack of explicit semantic modeling of the source document and its summary. In this paper, we extend previous work on abstractive summarization using Abstract Meaning Representation (AMR) with a neural language generation stage which we guide using the source document. We demonstrate that this guidance improves summarization results by 7.4 and 10.5 points in ROUGE-2 using gold standard AMR parses and parses obtained from an off-the-shelf parser respectively. We also find that the summarization performance using the latter is 2 ROUGE-2 points higher than that of a well-established neural encoder-decoder approach trained on a larger dataset. Code is available at \url{this https URL}

Connected through hyperedge HE-1 (induced by source paper "joint parsing and generation for abstractive summarization"):
  with members:
    - "analyzing sentence fusion in abstractive summarization"
    - "controlling the amount of verbatim copying in abstractive summarization"
    - "scoring sentence singletons and pairs for abstractive summarization"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "generating wikipedia by summarizing long sequences"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-2 (induced by source paper "controlling the amount of verbatim copying in abstractive summarization"):
  with members:
    - "sample efficient text summarization using a single pre trained transformer"
    - "biset bi directional selective encoding with template for abstractive summarization"
    - "bertscore evaluating text generation with bert"
    - "unified language model pre training for natural language understanding and generation"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "core semantic first a top down approach for amr parsing"):
  with members:
    - "compositional semantic parsing across graphbanks"
    - "rewarding smatch transition based amr parsing with reinforcement learning"
    - "toward abstractive summarization using semantic representations"
    - "amr dependency parsing with a typed semantic algebra"
    - "an amr aligner tuned by transition based parser"
    - "amr parsing as graph prediction with latent alignment"
    - "linguistically informed self attention for semantic role labeling"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "sema an extended semantic evaluation metric for amr"):
  with members:
    - "amr parsing as graph prediction with latent alignment"
    - "neural semantic parsing by character based translation experiments with abstract meaning representations"
    - "an incremental parser for abstract meaning representation"

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)