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: "incorporating literals into knowledge graph embeddings"
  Abstract: Knowledge graphs, on top of entities and their relationships, contain another important element: literals. Literals encode interesting properties (e.g. the height) of entities that are not captured by links between entities alone. Most of the existing work on embedding (or latent feature) based knowledge graph modeling focuses mainly on the relations between entities. In this work, we study the effect of incorporating literal information into existing knowledge graph models. Our approach, which we name LiteralE, is an extension that can be plugged into existing latent feature methods. LiteralE merges entity embeddings with their literal information using a learnable, parametrized function, such as a simple linear or nonlinear transformation, or a multilayer neural network. We extend several popular embedding models using LiteralE and evaluate the performance on the task of link prediction. Despite its simplicity, LiteralE proves to be an effective way to incorporate literal information into existing embedding based models, improving their performance on different standard datasets, which we augmented with their literals and provide as testbed for further research.

Connected through hyperedge HE-1 (induced by source paper "joint embedding learning of educational knowledge graphs"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "knowledge forest a novel model to organize knowledge fragments"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "character word lstm language models"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "a survey on knowledge graph embeddings with literals which model links better literal ly"):
  with members:
    - "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"
    - "embedding multimodal relational data for knowledge base completion"
    - "a first experiment on including text literals in kglove"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "multi task neural network for non discrete attribute prediction in knowledge graphs"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "complex embeddings for simple link prediction"

Connected through hyperedge HE-3 (induced by source paper "knowledge based distant regularization in learning probabilistic models"):
  with members:
    - "answering visual relational queries in web extracted knowledge graphs"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "learning the structure of generative models without labeled data"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "fast robust pca on graphs"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "bayesian inference with posterior regularization and applications to infinite latent svms"

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.AI(Artificial Intelligence)