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: "f score driven max margin neural network for named entity recognition in chinese social media"
  Abstract: We focus on named entity recognition (NER) for Chinese social media. With massive unlabeled text and quite limited labelled corpus, we propose a semi-supervised learning model based on B-LSTM neural network. To take advantage of traditional methods in NER such as CRF, we combine transition probability with deep learning in our model. To bridge the gap between label accuracy and F-score of NER, we construct a model which can be directly trained on F-score. When considering the instability of F-score driven method and meaningful information provided by label accuracy, we propose an integrated method to train on both F-score and label accuracy. Our integrated model yields 7.44\% improvement over previous state-of-the-art result.

Connected through hyperedge HE-1 (induced by source paper "self attention based bigru and capsule network for named entity recognition"):
  with members:
    - "can ner convolutional attention network for chinese named entity recognition"
    - "chinese ner using lattice lstm"
    - "investigating capsule networks with dynamic routing for text classification"
    - "a survey on deep learning for named entity recognition"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "fast and accurate entity recognition with iterated dilated convolutions"

Connected through hyperedge HE-2 (induced by source paper "cluener2020 fine grained named entity recognition dataset and benchmark for chinese"):
  with members:
    - "albert a lite bert for self supervised learning of language representations"
    - "ernie 2 0 a continual pre training framework for language understanding"
    - "pre training with whole word masking for chinese bert"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "learning named entity tagger using domain specific dictionary"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-3 (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"
    - "learning word segmentation representations to improve named entity recognition for 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-4 (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-5 (induced by source paper "porous lattice based transformer encoder for chinese ner"):
  with members:
    - "neural chinese named entity recognition via cnn lstm crf and joint training with word segmentation"
    - "lattice transformer for speech translation"
    - "lattice based transformer encoder for neural machine translation"
    - "self attentional models for lattice inputs"
    - "neural collective entity linking based on recurrent random walk network learning"
    - "chinese ner using lattice lstm"
    - "modeling localness for self attention networks"
    - "convolutional self attention network"

Connected through hyperedge HE-6 (induced by source paper "simplify the usage of lexicon in chinese ner"):
  with members:
    - "chinese ner using lattice lstm"
    - "combining discrete and neural features for sequence labeling"
    - "attention is all you need"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "empower sequence labeling with task aware neural language model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "improving named entity recognition for chinese social media with word segmentation representation learning"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-7 (induced by source paper "exploiting multiple embeddings for chinese named entity recognition"):
  with members:
    - "chinese ner using lattice lstm"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "improving named entity recognition for chinese social media with word segmentation representation learning"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-8 (induced by source paper "chinese ner using lattice lstm"):
  with members:
    - "cross sentence n ary relation extraction with graph lstms"
    - "combining discrete and neural features for sequence labeling"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised multitask learning for sequence labeling"
    - "semi supervised sequence tagging with bidirectional language models"
    - "lattice long short term memory for human action recognition"
    - "yedda a lightweight collaborative text span annotation tool"

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)