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: "sequence labeling a practical approach"
  Abstract: We take a practical approach to solving sequence labeling problem assuming unavailability of domain expertise and scarcity of informational and computational resources. To this end, we utilize a universal end-to-end Bi-LSTM-based neural sequence labeling model applicable to a wide range of NLP tasks and languages. The model combines morphological, semantic, and structural cues extracted from data to arrive at informed predictions. The model's performance is evaluated on eight benchmark datasets (covering three tasks: POS-tagging, NER, and Chunking, and four languages: English, German, Dutch, and Spanish). We observe state-of-the-art results on four of them: CoNLL-2012 (English NER), CoNLL-2002 (Dutch NER), GermEval 2014 (German NER), Tiger Corpus (German POS-tagging), and competitive performance on the rest.

Connected through hyperedge HE-1 (induced by source paper "multi scale self attention for text classification"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "star transformer"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "self attention with relative position representations"
    - "modeling localness for self attention networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "convolutional self attention network"

Connected through hyperedge HE-2 (induced by source paper "star transformer"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "multi task learning over graph structures"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "contextualized non local neural networks for sequence learning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dynamic self attention computing attention over words dynamically for sentence embedding"
    - "supervised learning of universal sentence representations from natural language inference data"

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"
    - "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"
    - "hybrid semi markov crf for neural sequence labeling"

Connected through hyperedge HE-4 (induced by source paper "few shot classification in named entity recognition task"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "label aware double transfer learning for cross specialty medical named entity recognition"
    - "learning how to self learn enhancing self training using neural reinforcement learning"
    - "neural cross lingual named entity recognition with minimal resources"
    - "deep active learning for named entity recognition"
    - "prototypical networks for few shot learning"
    - "improving one shot learning through fusing side information"

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)