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: "from one point to a manifold knowledge graph embedding for precise link prediction"
  Abstract: Knowledge graph embedding aims at offering a numerical knowledge representation paradigm by transforming the entities and relations into continuous vector space. However, existing methods could not characterize the knowledge graph in a fine degree to make a precise prediction. There are two reasons: being an ill-posed algebraic system and applying an overstrict geometric form. As precise prediction is critical, we propose an manifold-based embedding principle (\textbf{ManifoldE}) which could be treated as a well-posed algebraic system that expands the position of golden triples from one point in current models to a manifold in ours. Extensive experiments show that the proposed models achieve substantial improvements against the state-of-the-art baselines especially for the precise prediction task, and yet maintain high efficiency.

Connected through hyperedge HE-1 (induced by source paper "an evaluation of knowledge graph embeddings for autonomous driving data experience and practice"):
  with members:
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "knowledge graph embedding for ecotoxicological effect prediction"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"
    - "holographic embeddings of knowledge graphs"

Connected through hyperedge HE-2 (induced by source paper "knowledge enhanced contextual word representations"):
  with members:
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "wic the word in context dataset for evaluating context sensitive meaning representations"
    - "matching the blanks distributional similarity for relation learning"
    - "ernie enhanced language representation with informative entities"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "tucker tensor factorization for knowledge graph completion"
    - "simple bert models for relation extraction and semantic role labeling"

Connected through hyperedge HE-3 (induced by source paper "hyperkg hyperbolic knowledge graph embeddings for knowledge base completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "multi relational poincar e graph embeddings"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "hyperbolic neural networks"
    - "from knowledge graph embedding to ontology embedding an analysis of the compatibility between vector space representations and rules"

Connected through hyperedge HE-4 (induced by source paper "learning hierarchy aware knowledge graph embeddings for link prediction"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "ernie enhanced language representation with informative entities"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "a capsule network based embedding model for knowledge graph completion and search personalization"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"

Connected through hyperedge HE-5 (induced by source paper "relational memory based knowledge graph embedding"):
  with members:
    - "relational recurrent neural networks"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "attention is all you need"
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "convolutional 2d knowledge graph embeddings"
    - "neighborhood mixture model for knowledge base completion"
    - "complex embeddings for simple link prediction"
    - "knowlege graph embedding by flexible translation"

Connected through hyperedge HE-6 (induced by source paper "domain representation for knowledge graph embedding"):
  with members:
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"
    - "an interpretable knowledge transfer model for knowledge base completion"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "proje embedding projection for knowledge graph completion"
    - "modeling relation paths for representation learning of knowledge bases"
    - "locally adaptive translation for knowledge graph embedding"
    - "holographic embeddings of knowledge graphs"

Connected through hyperedge HE-7 (induced by source paper "composing knowledge graph embeddings via word embeddings"):
  with members:
    - "incorporating gan for negative sampling in knowledge representation learning"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "toruse knowledge graph embedding on a lie group"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"
    - "transg a generative mixture model for knowledge graph embedding"

Connected through hyperedge HE-8 (induced by source paper "towards combinational relation linking over knowledge graphs"):
  with members:
    - "graph pattern entity ranking model for knowledge graph completion"
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "toruse knowledge graph embedding on a lie group"
    - "explainable entity based recommendations with knowledge graphs"
    - "modeling relation paths for representation learning of knowledge bases"

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)