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: "learning word segmentation representations to improve named entity recognition for chinese social media"
  Abstract: Named entity recognition, and other information extraction tasks, frequently use linguistic features such as part of speech tags or chunkings. For languages where word boundaries are not readily identified in text, word segmentation is a key first step to generating features for an NER system. While using word boundary tags as features are helpful, the signals that aid in identifying these boundaries may provide richer information for an NER system. New state-of-the-art word segmentation systems use neural models to learn representations for predicting word boundaries. We show that these same representations, jointly trained with an NER system, yield significant improvements in NER for Chinese social media. In our experiments, jointly training NER and word segmentation with an LSTM-CRF model yields nearly 5% absolute improvement over previously published results.

Connected through hyperedge HE-1 (induced by source paper "integrating boundary assembling into a dnn framework for named entity recognition in chinese social media text"):
  with members:
    - "chinese ner using lattice lstm"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "f score driven max margin neural network for named entity recognition in chinese social media"
    - "neural word segmentation learning for chinese"
    - "improving named entity recognition for chinese social media with word segmentation representation learning"
    - "neural architectures for named entity recognition"
    - "named entity recognition with bidirectional lstm cnns"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "can ner convolutional attention network for chinese named entity recognition"):
  with members:
    - "grn gated relation network to enhance convolutional neural network for named entity recognition"
    - "analogical reasoning on chinese morphological and semantic relations"
    - "chinese ner using lattice lstm"
    - "design challenges and misconceptions in neural sequence labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial learning for chinese ner from crowd annotations"
    - "combining discrete and neural features for sequence labeling"

Connected through hyperedge HE-3 (induced by source paper "back attention knowledge transfer for low resource named entity recognition"):
  with members:
    - "linguistic knowledge and transferability of contextual representations"
    - "chinese ner using lattice lstm"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "beam search strategies for neural machine translation"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "convolutional sequence to sequence learning"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-4 (induced by source paper "dependency aware named entity recognition with relative and global attentions"):
  with members:
    - "modeling noisiness to recognize named entities using multitask neural networks on social media"
    - "design challenges and misconceptions in neural sequence labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "efficient dependency guided named entity recognition"
    - "attention is all you need"
    - "empower sequence labeling with task aware neural language model"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-5 (induced by source paper "f score driven max margin neural network for named entity recognition in chinese social media"):
  with members:
    - "improving named entity recognition for chinese social media with word segmentation representation learning"
    - "learning dictionaries for named entity recognition using minimal supervision"
    - "bidirectional lstm crf models for sequence tagging"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

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)