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: "a survey on deep learning for named entity recognition"
  Abstract: Named entity recognition (NER) is the task to identify text spans that mention named entities, and to classify them into predefined categories such as person, location, organization etc. NER serves as the basis for a variety of natural language applications such as question answering, text summarization, and machine translation. Although early NER systems are successful in producing decent recognition accuracy, they often require much human effort in carefully designing rules or features. In recent years, deep learning, empowered by continuous real-valued vector representations and semantic composition through nonlinear processing, has been employed in NER systems, yielding stat-of-the-art performance. In this paper, we provide a comprehensive review on existing deep learning techniques for NER. We first introduce NER resources, including tagged NER corpora and off-the-shelf NER tools. Then, we systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder. Next, we survey the most representative methods for recent applied techniques of deep learning in new NER problem settings and applications. Finally, we present readers with the challenges faced by NER systems and outline future directions in this area.

Connected through hyperedge HE-1 (induced by source paper "self attention based bigru and capsule network for named entity recognition"):
  with members:
    - "can ner convolutional attention network for chinese named entity recognition"
    - "chinese ner using lattice lstm"
    - "investigating capsule networks with dynamic routing for text classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "dynamic routing between capsules"

Connected through hyperedge HE-2 (induced by source paper "chinese named entity recognition augmented with lexicon memory"):
  with members:
    - "chinese ner using lattice lstm"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ncrf an open source neural sequence labeling toolkit"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "combining discrete and neural features for sequence labeling"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-3 (induced by source paper "named entity recognition is there a glass ceiling"):
  with members:
    - "graph convolution for multimodal information extraction from visually rich documents"
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "chargrid towards understanding 2d documents"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "a survey on open information extraction"
    - "snorkel rapid training data creation with weak supervision"

Connected through hyperedge HE-4 (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 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"
    - "swellshark a generative model for biomedical named entity recognition without labeled data"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-5 (induced by source paper "healthcare ner models using language model pretraining"):
  with members:
    - "scispacy fast and robust models for biomedical natural language processing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural architectures for named entity recognition"
    - "distributed representations of words and phrases and their compositionality"

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)