human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "clinical text summarization with syntax based negation and semantic concept identification"):
  Abstract: In the era of clinical information explosion, a good strategy for clinical text summarization is helpful to improve the clinical workflow. The ideal summarization strategy can preserve important information in the informative but less organized, ill-structured clinical narrative texts. Instead of using pure statistical learning approaches, which are difficult to interpret and explain, we utilized knowledge of computational linguistics with human experts-curated biomedical knowledge base to achieve the interpretable and meaningful clinical text summarization. Our research objective is to use the biomedical ontology with semantic information, and take the advantage from the language hierarchical structure, the constituency tree, in order to identify the correct clinical concepts and the corresponding negation information, which is critical for summarizing clinical concepts from narrative text. We achieved the clinically acceptable performance for both negation detection and concept identification, and the clinical concepts with common negated patterns can be identified and negated by the proposed method.

Includes members:
  Member "a deep reinforced model for abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "time aware large kernel convolutions")
  Member "abstractive text summarization using sequence to sequence rnns and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training")
  Member "the mythos of model interpretability":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "building machines that learn and think like people":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "a neural attention model for abstractive sentence summarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "improved semantic representations from tree structured long short term memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unbiased scene graph generation from biased training")
  Member "sequence to sequence learning with neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

Question: Please tell me which class the source paper 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)