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 multi relational link prediction with bilinear models"
  Abstract: We study bilinear embedding models for the task of multi-relational link prediction and knowledge graph completion. Bilinear models belong to the most basic models for this task, they are comparably efficient to train and use, and they can provide good prediction performance. The main goal of this paper is to explore the expressiveness of and the connections between various bilinear models proposed in the literature. In particular, a substantial number of models can be represented as bilinear models with certain additional constraints enforced on the embeddings. We explore whether or not these constraints lead to universal models, which can in principle represent every set of relations, and whether or not there are subsumption relationships between various models. We report results of an independent experimental study that evaluates recent bilinear models in a common experimental setup. Finally, we provide evidence that relation-level ensembles of multiple bilinear models can achieve state-of-the art prediction performance.

Connected through hyperedge HE-1 (induced by source paper "rdfframes knowledge graph access for machine learning tools"):
  with members:
    - "mapping rdf graphs to property graphs"
    - "g core a core for future graph query languages"
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "convolutional 2d knowledge graph embeddings"
    - "semantics and complexity of sparql"

Connected through hyperedge HE-2 (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-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"
    - "on evaluating embedding models for knowledge base completion"

Connected through hyperedge HE-4 (induced by source paper "logicenn a neural based knowledge graphs embedding model with logical rules"):
  with members:
    - "quaternion knowledge graph embedding"
    - "improving knowledge graph embedding using simple constraints"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "adversarial sets for regularising neural link predictors"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"
    - "lifted rule injection for relation embeddings"

Connected through hyperedge HE-5 (induced by source paper "binarized canonical polyadic decomposition for knowledge graph completion"):
  with members:
    - "data dependent learning of symmetric antisymmetric relations for knowledge base completion"
    - "a survey on methods and theories of quantized neural networks"
    - "word2bits quantized word vectors"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "knowledge base completion baselines strike back"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"

Connected through hyperedge HE-6 (induced by source paper "mde multiple distance embeddings for link prediction in knowledge graphs"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "convolutional 2d knowledge graph embeddings"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"

Connected through hyperedge HE-7 (induced by source paper "analyzing knowledge graph embedding methods from a multi embedding interaction perspective"):
  with members:
    - "translation based recommendation"
    - "convolutional 2d knowledge graph embeddings"
    - "on complex valued convolutional neural networks"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"
    - "transa an adaptive approach for knowledge graph embedding"
    - "adam a method for stochastic optimization"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-8 (induced by source paper "on the knowledge graph completion using translation based embedding the loss is as important as the score"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "soft marginal transe for scholarly knowledge graph completion"
    - "improved knowledge graph embedding using background taxonomic information"
    - "improving knowledge graph embedding using simple constraints"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "toruse knowledge graph embedding on a lie group"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"

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.LG(Machine Learning)