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: "on evaluating embedding models for knowledge base completion"
  Abstract: Knowledge bases contribute to many web search and mining tasks, yet they are often incomplete. To add missing facts to a given knowledge base, various embedding models have been proposed in the recent literature. Perhaps surprisingly, relatively simple models with limited expressiveness often performed remarkably well under today's most commonly used evaluation protocols. In this paper, we explore whether recent models work well for knowledge base completion and argue that the current evaluation protocols are more suited for question answering rather than knowledge base completion. We show that when focusing on a different prediction task for evaluating knowledge base completion, the performance of current embedding models is unsatisfactory even on datasets previously thought to be too easy. This is especially true when embedding models are compared against a simple rule-based baseline. This work indicates the need for more research into the embedding models and evaluation protocols for knowledge base completion.

Connected through hyperedge HE-1 (induced by source paper "knowledge graph embedding for link prediction a comparative analysis"):
  with members:
    - "analyzing knowledge graph embedding methods from a multi embedding interaction perspective"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "tucker tensor factorization for knowledge graph completion"
    - "interaction embeddings for prediction and explanation in knowledge graphs"
    - "a capsule network based embedding model for knowledge graph completion and search personalization"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "toruse knowledge graph embedding on a lie group"

Connected through hyperedge HE-2 (induced by source paper "realistic re evaluation of knowledge graph completion methods an experimental study"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "convolutional 2d knowledge graph embeddings"
    - "proje embedding projection for knowledge graph completion"
    - "complex embeddings for simple link prediction"

Connected through hyperedge HE-3 (induced by source paper "autokge searching scoring functions for knowledge graph embedding"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "searching for interaction functions in collaborative filtering"
    - "nscaching simple and efficient negative sampling for knowledge graph embedding"
    - "taking human out of learning applications a survey on automated machine learning"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "knowledge representation learning a quantitative review"

Connected through hyperedge HE-4 (induced by source paper "nscaching simple and efficient negative sampling for knowledge graph embedding"):
  with members:
    - "incorporating gan for negative sampling in knowledge representation learning"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "analogical inference for multi relational embeddings"
    - "node2vec scalable feature learning for networks"
    - "complex embeddings for simple link prediction"
    - "from one point to a manifold knowledge graph embedding for precise link prediction"

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)