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: "improved knowledge graph embedding using background taxonomic information"
  Abstract: Knowledge graphs are used to represent relational information in terms of triples. To enable learning about domains, embedding models, such as tensor factorization models, can be used to make predictions of new triples. Often there is background taxonomic information (in terms of subclasses and subproperties) that should also be taken into account. We show that existing fully expressive (a.k.a. universal) models cannot provably respect subclass and subproperty information. We show that minimal modifications to an existing knowledge graph completion method enables injection of taxonomic information. Moreover, we prove that our model is fully expressive, assuming a lower-bound on the size of the embeddings. Experimental results on public knowledge graphs show that despite its simplicity our approach is surprisingly effective.

Connected through hyperedge HE-1 (induced by source paper "diachronic embedding for temporal knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "hypernetwork knowledge graph embeddings"
    - "tucker tensor factorization for knowledge graph completion"
    - "how powerful are graph neural networks"
    - "learning dynamic embeddings from temporal interactions"
    - "learning sequence encoders for temporal knowledge graph completion"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"

Connected through hyperedge HE-2 (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"
    - "improving knowledge graph embedding using simple constraints"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "on multi relational link prediction with bilinear models"
    - "toruse knowledge graph embedding on a lie group"
    - "analogical inference for multi relational embeddings"
    - "convolutional 2d knowledge graph embeddings"

Connected through hyperedge HE-3 (induced by source paper "knowledge hypergraphs prediction beyond binary relations"):
  with members:
    - "hypernetwork knowledge graph embeddings"
    - "hypergraph neural networks"
    - "hypergcn a new method of training graph convolutional networks on hypergraphs"
    - "how powerful are graph neural networks"
    - "knowledge graph completion via complex tensor factorization"
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "knowledge base completion baselines strike back"
    - "convolutional 2d knowledge graph embeddings"

Connected through hyperedge HE-4 (induced by source paper "toward understanding the effect of loss function on then performance of knowledge graph embedding"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "soft marginal transe for scholarly knowledge graph completion"
    - "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"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"

Connected through hyperedge HE-5 (induced by source paper "learning high order structural and attribute information by knowledge graph attention networks for enhancing knowledge graph embedding"):
  with members:
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "improving knowledge graph embedding using simple constraints"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "cross lingual entity alignment via joint attribute preserving embedding"
    - "semi supervised classification with graph convolutional networks"
    - "modeling relation paths for representation learning of knowledge bases"
    - "distributed representations of words and phrases and their compositionality"

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)