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: "hybrid semi markov crf for neural sequence labeling"
  Abstract: This paper proposes hybrid semi-Markov conditional random fields (SCRFs) for neural sequence labeling in natural language processing. Based on conventional conditional random fields (CRFs), SCRFs have been designed for the tasks of assigning labels to segments by extracting features from and describing transitions between segments instead of words. In this paper, we improve the existing SCRF methods by employing word-level and segment-level information simultaneously. First, word-level labels are utilized to derive the segment scores in SCRFs. Second, a CRF output layer and an SCRF output layer are integrated into an unified neural network and trained jointly. Experimental results on CoNLL 2003 named entity recognition (NER) shared task show that our model achieves state-of-the-art performance when no external knowledge is used.

Connected through hyperedge HE-1 (induced by source paper "dsreg using distant supervision as a regularizer"):
  with members:
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "syntactic scaffolds for semantic structures"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "attention is all you need"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "the narrativeqa reading comprehension challenge"

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"
    - "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"
    - "semi supervised multitask learning for sequence labeling"

Connected through hyperedge HE-3 (induced by source paper "tener adapting transformer encoder for named entity recognition"):
  with members:
    - "can ner convolutional attention network for chinese named entity recognition"
    - "grn gated relation network to enhance convolutional neural network for named entity recognition"
    - "transformer xl attentive language models beyond a fixed length context"
    - "sequence labeling a practical approach"
    - "chinese ner using lattice lstm"
    - "self attention with relative position representations"
    - "robust lexical features for improved neural network named entity recognition"
    - "contextualized non local neural networks for sequence learning"

Connected through hyperedge HE-4 (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"

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)