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 "joint entity extraction and assertion detection for clinical text"):
  Abstract: Negative medical findings are prevalent in clinical reports, yet discriminating them from positive findings remains a challenging task for information extraction. Most of the existing systems treat this task as a pipeline of two separate tasks, i.e., named entity recognition (NER) and rule-based negation detection. We consider this as a multi-task problem and present a novel end-to-end neural model to jointly extract entities and negations. We extend a standard hierarchical encoder-decoder NER model and first adopt a shared encoder followed by separate decoders for the two tasks. This architecture performs considerably better than the previous rule-based and machine learning-based systems. To overcome the problem of increased parameter size especially for low-resource settings, we propose the Conditional Softmax Shared Decoder architecture which achieves state-of-art results for NER and negation detection on the 2010 i2b2/VA challenge dataset and a proprietary de-identified clinical dataset.

Includes members:
  Member "relation extraction using explicit context conditioning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "assessment of amazon comprehend medical medication information extraction")
  Member "comprehend medical a named entity recognition and relationship extraction web service":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding patient complaint characteristics using contextual clinical bert embeddings")
  Member "dynamic transfer learning for named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "assessment of amazon comprehend medical medication information extraction")
  Member "improving hospital mortality prediction with medical named entities and multimodal learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "assessment of amazon comprehend medical medication information extraction")
  Member "negbio a high performance tool for negation and uncertainty detection in radiology reports":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "when radiology report generation meets knowledge graph")
  Member "morphological priors for probabilistic neural word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nytwit a dataset of novel words in the new york times")
  Member "enriching word vectors with subword information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "multi task cross lingual sequence tagging from scratch":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi task learning for argumentation mining")

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)