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 named entity tagger using domain specific dictionary"):
  Abstract: Recent advances in deep neural models allow us to build reliable named entity recognition (NER) systems without handcrafting features. However, such methods require large amounts of manually-labeled training data. There have been efforts on replacing human annotations with distant supervision (in conjunction with external dictionaries), but the generated noisy labels pose significant challenges on learning effective neural models. Here we propose two neural models to suit noisy distant supervision from the dictionary. First, under the traditional sequence labeling framework, we propose a revised fuzzy CRF layer to handle tokens with multiple possible labels. After identifying the nature of noisy labels in distant supervision, we go beyond the traditional framework and propose a novel, more effective neural model AutoNER with a new Tie or Break scheme. In addition, we discuss how to refine distant supervision for better NER performance. Extensive experiments on three benchmark datasets demonstrate that AutoNER achieves the best performance when only using dictionaries with no additional human effort, and delivers competitive results with state-of-the-art supervised benchmarks.

Includes members:
  Member "cross type biomedical named entity recognition with deep multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mt bioner multi task learning for biomedical named entity recognition using deep bidirectional transformers")
  Member "automated phrase mining from massive text corpora":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cube2net efficient query specific network construction with data cube organization")
  Member "unsupervised aspect term extraction with b lstm crf using automatically labelled datasets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hamner headword amplified multi span distantly supervised method for domain specific named entity recognition")
  Member "swellshark a generative model for biomedical named entity recognition without labeled data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "few shot classification in named entity recognition task")
  Member "empower sequence labeling with task aware neural language model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "keeping it simple implementation and performance of the proto principle of adaptation and learning in the language sciences")
  Member "recurrent neural network models for disease name recognition using domain invariant features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "collabonet collaboration of deep neural networks for biomedical named entity recognition")
  Member "end to end sequence labeling via bi directional lstm cnns crf":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "neural architectures for named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "knowledge graphs")

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)