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: "neural reranking for named entity recognition"
  Abstract: We propose a neural reranking system for named entity recognition (NER). The basic idea is to leverage recurrent neural network models to learn sentence-level patterns that involve named entity mentions. In particular, given an output sentence produced by a baseline NER model, we replace all entity mentions, such as \textit{Barack Obama}, into their entity types, such as \textit{PER}. The resulting sentence patterns contain direct output information, yet is less sparse without specific named entities. For example, "PER was born in LOC" can be such a pattern. LSTM and CNN structures are utilised for learning deep representations of such sentences for reranking. Results show that our system can significantly improve the NER accuracies over two different baselines, giving the best reported results on a standard benchmark.

Connected through hyperedge HE-1 (induced by source paper "hierarchical contextualized representation for named entity recognition"):
  with members:
    - "semantics aware bert for language understanding"
    - "grn gated relation network to enhance convolutional neural network for named entity recognition"
    - "syntax aware multilingual semantic role labeling"
    - "lattice based transformer encoder for neural machine translation"
    - "gcdt a global context enhanced deep transition architecture for sequence labeling"
    - "dual co matching network for multi choice reading comprehension"
    - "robust lexical features for improved neural network named entity recognition"
    - "contextualized non local neural networks for sequence learning"

Connected through hyperedge HE-2 (induced by source paper "gcdt a global context enhanced deep transition architecture for sequence labeling"):
  with members:
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "attention is all you need"
    - "neural models for sequence chunking"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised multitask learning for sequence labeling"

Connected through hyperedge HE-3 (induced by source paper "a survey on deep learning for named entity recognition"):
  with members:
    - "learning a unified named entity tagger from multiple partially annotated corpora for efficient adaptation"
    - "a unified mrc framework for named entity recognition"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "a little annotation does a lot of good a study in bootstrapping low resource named entity recognizers"
    - "dependency guided lstm crf for named entity recognition"
    - "hierarchical contextualized representation for named entity recognition"
    - "dice loss for data imbalanced nlp tasks"
    - "attending to entities for better text understanding"

Connected through hyperedge HE-4 (induced by source paper "design challenges and misconceptions in neural sequence labeling"):
  with members:
    - "neural models for sequence chunking"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised multitask learning for sequence labeling"
    - "semi supervised sequence tagging with bidirectional language models"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "empower sequence labeling with task aware neural language model"

Connected through hyperedge HE-5 (induced by source paper "robust lexical features for improved neural network named entity recognition"):
  with members:
    - "deep contextualized word representations"
    - "deep active learning for named entity recognition"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "semi supervised sequence tagging with bidirectional language models"
    - "named entity recognition with stack residual lstm and trainable bias decoding"
    - "empower sequence labeling with task aware neural language model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-6 (induced by source paper "evaluating the utility of hand crafted features in sequence labelling"):
  with members:
    - "narrative modeling with memory chains and semantic supervision"
    - "deep contextualized word representations"
    - "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "modeling source syntax for neural machine translation"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"

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)