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: "sentilr linguistic knowledge enhanced language representation for sentiment analysis"
  Abstract: Most of the existing pre-trained language representation models neglect to consider the linguistic knowledge of texts, whereas we argue that such knowledge can promote language understanding in various NLP tasks. In this paper, we propose a novel language representation model called SentiLR, which introduces word-level linguistic knowledge including part-of-speech tag and prior sentiment polarity from SentiWordNet to benefit the downstream tasks in sentiment analysis. During pre-training, we first acquire the prior sentiment polarity of each word by querying the SentiWordNet dictionary with its part-of-speech tag. Then, we devise a new pre-training task called label-aware masked language model (LA-MLM) consisting of two subtasks: 1) word knowledge recovering given the sentence-level label; 2) sentence-level label prediction with linguistic knowledge enhanced context. Experiments show that SentiLR achieves state-of-the-art performance on several sentence-level / aspect-level sentiment analysis tasks by fine-tuning, and also obtain comparative results on general language understanding tasks.

Connected through hyperedge HE-1 (induced by source paper "a pre training based personalized dialogue generation model with persona sparse data"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "pretraining methods for dialog context representation learning"
    - "ernie enhanced representation through knowledge integration"
    - "exploiting persona information for diverse generation of conversational responses"
    - "challenges in building intelligent open domain dialog systems"
    - "hello it s gpt 2 how can i help you towards the use of pretrained language models for task oriented dialogue systems"
    - "personalized dialogue generation with diversified traits"

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)