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: "swellshark a generative model for biomedical named entity recognition without labeled data"
  Abstract: We present SwellShark, a framework for building biomedical named entity recognition (NER) systems quickly and without hand-labeled data. Our approach views biomedical resources like lexicons as function primitives for autogenerating weak supervision. We then use a generative model to unify and denoise this supervision and construct large-scale, probabilistically labeled datasets for training high-accuracy NER taggers. In three biomedical NER tasks, SwellShark achieves competitive scores with state-of-the-art supervised benchmarks using no hand-labeled training data. In a drug name extraction task using patient medical records, one domain expert using SwellShark achieved within 5.1% of a crowdsourced annotation approach -- which originally utilized 20 teams over the course of several weeks -- in 24 hours.

Connected through hyperedge HE-1 (induced by source paper "few shot classification in named entity recognition task"):
  with members:
    - "sequence labeling a practical approach"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "label aware double transfer learning for cross specialty medical named entity recognition"
    - "learning how to self learn enhancing self training using neural reinforcement learning"
    - "neural cross lingual named entity recognition with minimal resources"
    - "deep active learning for named entity recognition"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "named entity recognition only from word embeddings"):
  with members:
    - "knowledge augmented language model and its application to unsupervised named entity recognition"
    - "design challenges and misconceptions in neural sequence labeling"
    - "reinforcement learning for relation classification from noisy data"
    - "ncrf an open source neural sequence labeling toolkit"
    - "unsupervised learning of syntactic structure with invertible neural projections"
    - "deep active learning for named entity recognition"
    - "empower sequence labeling with task aware neural language model"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-3 (induced by source paper "hamner headword amplified multi span distantly supervised method for domain specific named entity recognition"):
  with members:
    - "multi grained named entity recognition"
    - "a survey on deep learning for named entity recognition"
    - "a span selection model for semantic role labeling"
    - "deep contextualized word representations"
    - "automated phrase mining from massive text corpora"
    - "unsupervised aspect term extraction with b lstm crf using automatically labelled datasets"
    - "a structured self attentive sentence embedding"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-4 (induced by source paper "biomedical named entity recognition via reference set augmented bootstrapping"):
  with members:
    - "nseen neural semantic embedding for entity normalization"
    - "learning named entity tagger using domain specific dictionary"
    - "automated phrase mining from massive text corpora"
    - "snorkel rapid training data creation with weak supervision"
    - "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-5 (induced by source paper "named entity recognition with partially annotated training data"):
  with members:
    - "training a neural network in a low resource setting on automatically annotated noisy data"
    - "exploiting partially annotated data for temporal relation extraction"
    - "snorkel rapid training data creation with weak supervision"
    - "fidelity weighted learning"
    - "neural architectures for named entity recognition"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-6 (induced by source paper "terminologies augmented recurrent neural network model for clinical named entity recognition"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "semi supervised recurrent neural network for adverse drug reaction mention extraction"
    - "semi supervised knowledge extraction for detection of drugs and their effects"
    - "neural architectures for named entity recognition"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "learning named entity tagger using domain specific dictionary"):
  with members:
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "automated phrase mining from massive text corpora"
    - "unsupervised aspect term extraction with b lstm crf using automatically labelled datasets"
    - "empower sequence labeling with task aware neural language model"
    - "recurrent neural network models for disease name recognition using domain invariant features"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for 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)