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: "complex embeddings for simple link prediction"
  Abstract: In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases. As in previous studies, we propose to solve this problem through latent factorization. However, here we make use of complex valued embeddings. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. Compared to state-of-the-art models such as Neural Tensor Network and Holographic Embeddings, our approach based on complex embeddings is arguably simpler, as it only uses the Hermitian dot product, the complex counterpart of the standard dot product between real vectors. Our approach is scalable to large datasets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks.

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"
    - "on evaluating embedding models for knowledge base completion"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "entity context and relational paths for knowledge graph completion"):
  with members:
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "coke contextualized knowledge graph embedding"
    - "iteratively learning embeddings and rules for knowledge graph reasoning"
    - "position aware graph neural networks"
    - "gnnexplainer generating explanations for graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "efficient rule learning with template saturation for knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "rule guided compositional representation learning on knowledge graphs"
    - "learning attention based embeddings for relation prediction in knowledge graphs"
    - "learning hierarchy aware knowledge graph embeddings for link prediction"
    - "interacte improving convolution based knowledge graph embeddings by increasing feature interactions"
    - "tucker tensor factorization for knowledge graph completion"
    - "composition based multi relational graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "product knowledge graph embedding for e commerce"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "a multi task learning approach for improving product title compression with user search log data"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "multi task learning as multi objective optimization"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "attention is all you need"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"

Connected through hyperedge HE-5 (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"
    - "on evaluating embedding models for knowledge base completion"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "computationally tractable riemannian manifolds for graph embeddings"):
  with members:
    - "riemannian adaptive optimization methods"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "grassmannian learning embedding geometry awareness in shallow and deep learning"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "poincar e embeddings for learning hierarchical representations"
    - "node2vec scalable feature learning for networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep convolutional networks on graph structured data"

Connected through hyperedge HE-7 (induced by source paper "non parametric learning of lifted restricted boltzmann machines"):
  with members:
    - "structure learning for relational logistic regression an ensemble approach"
    - "relation networks for object detection"
    - "a simple neural network module for relational reasoning"
    - "learning to compare relation network for few shot learning"
    - "column networks for collective classification"
    - "learning convolutional neural networks for graphs"
    - "holographic embeddings of knowledge graphs"
    - "lifted relational neural networks"

Connected through hyperedge HE-8 (induced by source paper "error detection in knowledge graphs path ranking embeddings or both"):
  with members:
    - "logic rules powered knowledge graph embedding"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "does william shakespeare really write hamlet knowledge representation learning with confidence"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "proje embedding projection for knowledge graph completion"
    - "holographic embeddings of knowledge graphs"
    - "learning multi relational semantics using neural embedding models"

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)