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: "named entity recognition with stack residual lstm and trainable bias decoding"
  Abstract: Recurrent Neural Network models are the state-of-the-art for Named Entity Recognition (NER). We present two innovations to improve the performance of these models. The first innovation is the introduction of residual connections between the Stacked Recurrent Neural Network model to address the degradation problem of deep neural networks. The second innovation is a bias decoding mechanism that allows the trained system to adapt to non-differentiable and externally computed objectives, such as the entity-based F-measure. Our work improves the state-of-the-art results for both Spanish and English languages on the standard train/development/test split of the CoNLL 2003 Shared Task NER dataset.

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 "grn gated relation network to enhance convolutional neural network for named entity recognition"):
  with members:
    - "design challenges and misconceptions in neural sequence labeling"
    - "hybrid semi markov crf for neural sequence labeling"
    - "deep contextualized word representations"
    - "deep active learning for named entity recognition"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"

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 "cell aware stacked lstms for modeling sentences"):
  with members:
    - "on tree based neural sentence modeling"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "deep contextualized word representations"
    - "enhancing sentence embedding with generalized pooling"
    - "learning to generate reviews and discovering sentiment"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "deep learning with dynamic computation graphs"

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"
    - "neural reranking for named entity recognition"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "semi supervised sequence tagging with bidirectional language models"
    - "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 "hybrid semi markov crf for neural sequence labeling"):
  with members:
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"
    - "segmental recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "named entity sequence classification"):
  with members:
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"
    - "boosting named entity recognition with neural character embeddings"

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)