human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "fine tuning pre trained transformer language models to distantly supervised relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation")
  Member "grammatical analysis of pretrained sentence encoders with acceptability judgments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blimp a benchmark of linguistic minimal pairs for english")
  Member "mass masked sequence to sequence pre training for language generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on contextual embeddings")
  Member "matching the blanks distributional similarity for relation learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "k adapter infusing knowledge into pre trained models with adapters")
  Member "ernie enhanced language representation with informative entities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on contextual embeddings")
  Member "story ending prediction by transferable bert":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "temporal embeddings and transformer models for narrative text understanding")
  Member "xlnet generalized autoregressive pretraining for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")
  Member "bert post training for review reading comprehension and aspect based sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "snippext semi supervised opinion mining with augmented data")

Question: Please tell me which class the source paper 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)