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: "a new recurrent neural crf for learning non linear edge features"
  Abstract: Conditional Random Field (CRF) and recurrent neural models have achieved success in structured prediction. More recently, there is a marriage of CRF and recurrent neural models, so that we can gain from both non-linear dense features and globally normalized CRF objective. These recurrent neural CRF models mainly focus on encode node features in CRF undirected graphs. However, edge features prove important to CRF in structured prediction. In this work, we introduce a new recurrent neural CRF model, which learns non-linear edge features, and thus makes non-linear features encoded completely. We compare our model with different neural models in well-known structured prediction tasks. Experiments show that our model outperforms state-of-the-art methods in NP chunking, shallow parsing, Chinese word segmentation and POS tagging.

Connected through hyperedge HE-1 (induced by source paper "improving neural named entity recognition with gazetteers"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "named entity recognition with bidirectional lstm cnns"
    - "lexicon infused phrase embeddings for named entity resolution"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-2 (induced by source paper "enhancing neural sequence labeling with position aware self attention"):
  with members:
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "neural models for sequence chunking"
    - "deep semantic role labeling with self attention"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "does higher order lstm have better accuracy for segmenting and labeling sequence data"

Connected through hyperedge HE-3 (induced by source paper "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf"):
  with members:
    - "multi task multi domain representation learning for sequence tagging"
    - "enriching word vectors with subword information"
    - "charagram embedding words and sentences via character n grams"
    - "bidirectional lstm crf models for sequence tagging"
    - "segmental recurrent neural networks"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "efficient estimation of word representations in vector space"

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)