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 "learning a unified named entity tagger from multiple partially annotated corpora for efficient adaptation"):
  Abstract: Named entity recognition (NER) identifies typed entity mentions in raw text. While the task is well-established, there is no universally used tagset: often, datasets are annotated for use in downstream applications and accordingly only cover a small set of entity types relevant to a particular task. For instance, in the biomedical domain, one corpus might annotate genes, another chemicals, and another diseases---despite the texts in each corpus containing references to all three types of entities. In this paper, we propose a deep structured model to integrate these "partially annotated" datasets to jointly identify all entity types appearing in the training corpora. By leveraging multiple datasets, the model can learn robust input representations; by building a joint structured model, it avoids potential conflicts caused by combining several models' predictions at test time. Experiments show that the proposed model significantly outperforms strong multi-task learning baselines when training on multiple, partially annotated datasets and testing on datasets that contain tags from more than one of the training corpora.

Includes members:
  Member "learning named entity tagger using domain specific dictionary":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cluener2020 fine grained named entity recognition dataset and benchmark for chinese")
  Member "cross type biomedical named entity recognition with deep multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mt bioner multi task learning for biomedical named entity recognition using deep bidirectional transformers")
  Member "empower sequence labeling with task aware neural language model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "keeping it simple implementation and performance of the proto principle of adaptation and learning in the language sciences")
  Member "end to end sequence labeling via bi directional lstm cnns crf":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "multi task domain adaptation for sequence tagging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving pre trained multilingual models with vocabulary expansion")
  Member "neural architectures for named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "knowledge graphs")
  Member "introduction to the conll 2003 shared task language independent named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "realm retrieval augmented language model pre training")

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)