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: "global textual relation embedding for relational understanding"
  Abstract: Pre-trained embeddings such as word embeddings and sentence embeddings are fundamental tools facilitating a wide range of downstream NLP tasks. In this work, we investigate how to learn a general-purpose embedding of textual relations, defined as the shortest dependency path between entities. Textual relation embedding provides a level of knowledge between word/phrase level and sentence level, and we show that it can facilitate downstream tasks requiring relational understanding of the text. To learn such an embedding, we create the largest distant supervision dataset by linking the entire English ClueWeb09 corpus to Freebase. We use global co-occurrence statistics between textual and knowledge base relations as the supervision signal to train the embedding. Evaluation on two relational understanding tasks demonstrates the usefulness of the learned textual relation embedding. The data and code can be found at this https URL

Connected through hyperedge HE-1 (induced by source paper "improving distant supervised relation extraction by dynamic neural network"):
  with members:
    - "fine tuning pre trained transformer language models to distantly supervised relation extraction"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "few shot nlg with pre trained language model"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "data to text generation with content selection and planning"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "get to the point summarization with pointer generator networks"
    - "sequence level knowledge distillation"
    - "neural text generation from structured data with application to the biography domain"
    - "skip thought vectors"

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)