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: "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.

Connected through hyperedge HE-1 (induced by source paper "cluener2020 fine grained named entity recognition dataset and benchmark for chinese"):
  with members:
    - "albert a lite bert for self supervised learning of language representations"
    - "ernie 2 0 a continual pre training framework for language understanding"
    - "pre training with whole word masking for chinese bert"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "f score driven max margin neural network for named entity recognition in chinese social media"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-2 (induced by source paper "biomedical named entity recognition via reference set augmented bootstrapping"):
  with members:
    - "nseen neural semantic embedding for entity normalization"
    - "automated phrase mining from massive text corpora"
    - "snorkel rapid training data creation with weak supervision"
    - "swellshark a generative model for biomedical named entity recognition without labeled data"
    - "empower sequence labeling with task aware neural language model"
    - "neural architectures for named entity recognition"
    - "training deep neural networks on noisy labels with bootstrapping"
    - "analysis of semi supervised learning with the yarowsky algorithm"

Connected through hyperedge HE-3 (induced by source paper "learning a unified named entity tagger from multiple partially annotated corpora for efficient adaptation"):
  with members:
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "empower sequence labeling with task aware neural language model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "multi task domain adaptation for sequence tagging"
    - "neural architectures for named entity recognition"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

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)