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: "fine tuning pre trained transformer language models to distantly supervised relation extraction"
  Abstract: Distantly supervised relation extraction is widely used to extract relational facts from text, but suffers from noisy labels. Current relation extraction methods try to alleviate the noise by multi-instance learning and by providing supporting linguistic and contextual information to more efficiently guide the relation classification. While achieving state-of-the-art results, we observed these models to be biased towards recognizing a limited set of relations with high precision, while ignoring those in the long tail. To address this gap, we utilize a pre-trained language model, the OpenAI Generative Pre-trained Transformer (GPT) [Radford et al., 2018]. The GPT and similar models have been shown to capture semantic and syntactic features, and also a notable amount of "common-sense" knowledge, which we hypothesize are important features for recognizing a more diverse set of relations. By extending the GPT to the distantly supervised setting, and fine-tuning it on the NYT10 dataset, we show that it predicts a larger set of distinct relation types with high confidence. Manual and automated evaluation of our model shows that it achieves a state-of-the-art AUC score of 0.422 on the NYT10 dataset, and performs especially well at higher recall levels.

Connected through hyperedge HE-1 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "long and diverse text generation with planning based hierarchical variational model"
    - "the curious case of neural text degeneration"
    - "pretraining methods for dialog context representation learning"
    - "ernie enhanced representation through knowledge integration"
    - "do massively pretrained language models make better storytellers"
    - "mass masked sequence to sequence pre training for language generation"
    - "strategies for structuring story generation"

Connected through hyperedge HE-2 (induced by source paper "sentilr linguistic knowledge enhanced language representation for sentiment analysis"):
  with members:
    - "grammatical analysis of pretrained sentence encoders with acceptability judgments"
    - "mass masked sequence to sequence pre training for language generation"
    - "matching the blanks distributional similarity for relation learning"
    - "ernie enhanced language representation with informative entities"
    - "story ending prediction by transferable bert"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-3 (induced by source paper "improving distant supervised relation extraction by dynamic neural network"):
  with members:
    - "global textual relation embedding for relational understanding"
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "cross relation cross bag attention for distantly supervised relation extraction"
    - "distant supervision for relation extraction with linear attenuation simulation and non iid relevance embedding"
    - "multi labeled relation extraction with attentive capsule network"
    - "combining distant and direct supervision for neural relation extraction"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "reinforcement learning for relation classification from noisy data"

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)