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: "end to end sequence labeling via bi directional lstm cnns crf"
  Abstract: State-of-the-art sequence labeling systems traditionally require large amounts of task-specific knowledge in the form of hand-crafted features and data pre-processing. In this paper, we introduce a novel neutral network architecture that benefits from both word- and character-level representations automatically, by using combination of bidirectional LSTM, CNN and CRF. Our system is truly end-to-end, requiring no feature engineering or data pre-processing, thus making it applicable to a wide range of sequence labeling tasks. We evaluate our system on two data sets for two sequence labeling tasks --- Penn Treebank WSJ corpus for part-of-speech (POS) tagging and CoNLL 2003 corpus for named entity recognition (NER). We obtain state-of-the-art performance on both the two data --- 97.55\% accuracy for POS tagging and 91.21\% F1 for NER.

Connected through hyperedge HE-1 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing"):
  with members:
    - "classifying medical relations in clinical text via convolutional neural networks"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "explainable prediction of medical codes from clinical text"
    - "embedding transfer for low resource medical named entity recognition a case study on patient mobility"
    - "medical concept embedding with time aware attention"
    - "patient representation learning and interpretable evaluation using clinical notes"
    - "semantic structure and interpretability of word embeddings"
    - "improving medical short text classification with semantic expansion using word cluster embedding"

Connected through hyperedge HE-2 (induced by source paper "zero resource cross domain named entity recognition"):
  with members:
    - "do we need word order information for cross lingual sequence labeling"
    - "zero shot cross lingual dialogue systems with transferable latent variables"
    - "incorporating word and subword units in unsupervised machine translation using language model rescoring"
    - "attention informed mixed language training for zero shot cross lingual task oriented dialogue systems"
    - "robust zero shot cross domain slot filling with example values"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "label aware double transfer learning for cross specialty medical named entity recognition"
    - "neural adaptation layers for cross domain named entity recognition"

Connected through hyperedge HE-3 (induced by source paper "hybrid neural tagging model for open relation extraction"):
  with members:
    - "an open world extension to knowledge graph completion models"
    - "graph neural networks with generated parameters for relation extraction"
    - "openki integrating open information extraction and knowledge bases with relation inference"
    - "design challenges and misconceptions in neural sequence labeling"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "a survey on open information extraction"
    - "neural open information extraction"
    - "deep active learning for named entity recognition"

Connected through hyperedge HE-4 (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"
    - "a survey on deep learning for named entity recognition"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "domain independent extraction of scientific concepts from research articles"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "investigating correlations of inter coder agreement and machine annotation performance for historical video data"
    - "hierarchical neural networks for sequential sentence classification in medical scientific abstracts"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "allennlp a deep semantic natural language processing platform"
    - "construction of the literature graph in semantic scholar"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "end to end neural coreference resolution"

Connected through hyperedge HE-6 (induced by source paper "discontinuous constituent parsing with pointer networks"):
  with members:
    - "discontinuous constituency parsing with a stack free transition system and a dynamic oracle"
    - "left to right dependency parsing with pointer networks"
    - "unlexicalized transition based discontinuous constituency parsing"
    - "stack pointer networks for dependency parsing"
    - "simpler but more accurate semantic dependency parsing"
    - "recurrent neural network grammars"
    - "deep biaffine attention for neural dependency parsing"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

Connected through hyperedge HE-7 (induced by source paper "detecting potential topics in news using bert crf and wikipedia"):
  with members:
    - "ner and pos when nothing is capitalized"
    - "robust named entity recognition with truecasing pretraining"
    - "neural architectures for nested ner through linearization"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "enriching word vectors with subword information"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-8 (induced by source paper "computationally efficient ner taggers with combined embeddings and constrained decoding"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "deep active learning for named entity recognition"
    - "semi supervised sequence tagging with bidirectional language models"
    - "neural architectures for named entity recognition"
    - "named entity recognition with bidirectional lstm cnns"
    - "efficient estimation of word representations in vector space"

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.LG(Machine Learning)