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: "combining discrete and neural features for sequence labeling"
  Abstract: Neural network models have recently received heated research attention in the natural language processing community. Compared with traditional models with discrete features, neural models have two main advantages. First, they take low-dimensional, real-valued embedding vectors as inputs, which can be trained over large raw data, thereby addressing the issue of feature sparsity in discrete models. Second, deep neural networks can be used to automatically combine input features, and including non-local features that capture semantic patterns that cannot be expressed using discrete indicator features. As a result, neural network models have achieved competitive accuracies compared with the best discrete models for a range of NLP tasks. #R##N#On the other hand, manual feature templates have been carefully investigated for most NLP tasks over decades and typically cover the most useful indicator pattern for solving the problems. Such information can be complementary the features automatically induced from neural networks, and therefore combining discrete and neural features can potentially lead to better accuracy compared with models that leverage discrete or neural features only. #R##N#In this paper, we systematically investigate the effect of discrete and neural feature combination for a range of fundamental NLP tasks based on sequence labeling, including word segmentation, POS tagging and named entity recognition for Chinese and English, respectively. Our results on standard benchmarks show that state-of-the-art neural models can give accuracies comparable to the best discrete models in the literature for most tasks and combing discrete and neural features unanimously yield better results.

Connected through hyperedge HE-1 (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"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "chinese named entity recognition augmented with lexicon memory"):
  with members:
    - "chinese ner using lattice lstm"
    - "a survey on deep learning for named entity recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ncrf an open source neural sequence labeling toolkit"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-3 (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-4 (induced by source paper "simplify the usage of lexicon in chinese ner"):
  with members:
    - "chinese ner using lattice lstm"
    - "attention is all you need"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "empower sequence labeling with task aware neural language model"
    - "f score driven max margin neural network for named entity recognition in chinese social media"
    - "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-5 (induced by source paper "chinese ner using lattice lstm"):
  with members:
    - "cross sentence n ary relation extraction with graph lstms"
    - "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"
    - "neural word segmentation with rich pretraining"

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)