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: "a semantic matching energy function for learning with multi relational data"
  Abstract: Large-scale relational learning becomes crucial for handling the huge amounts of structured data generated daily in many application domains ranging from computational biology or information retrieval, to natural language processing. In this paper, we present a new neural network architecture designed to embed multi-relational graphs into a flexible continuous vector space in which the original data is kept and enhanced. The network is trained to encode the semantics of these graphs in order to assign high probabilities to plausible components. We empirically show that it reaches competitive performance in link prediction on standard datasets from the literature.

Connected through hyperedge HE-1 (induced by source paper "joint embedding learning of educational knowledge graphs"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "knowledge forest a novel model to organize knowledge fragments"
    - "incorporating literals into knowledge graph embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "character word lstm language models"

Connected through hyperedge HE-2 (induced by source paper "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "neural collective entity linking"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "learning over knowledge base embeddings for recommendation"
    - "joint representation learning of cross lingual words and entities via attentive distant supervision"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "adversarial personalized ranking for recommendation"
    - "explicit state tracking with semi supervisionfor neural dialogue generation"

Connected through hyperedge HE-3 (induced by source paper "scene graph prediction with limited labels"):
  with members:
    - "referring relationships"
    - "graph r cnn for scene graph generation"
    - "large scale visual relationship understanding"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "image generation from scene graphs"
    - "vip cnn visual phrase guided convolutional neural network"
    - "inferring and executing programs for visual reasoning"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-4 (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"
    - "on multi relational link prediction with bilinear models"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "knowledge graph fact prediction via knowledge enriched tensor factorization"):
  with members:
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "adversarial sets for regularising neural link predictors"
    - "knowledge base completion baselines strike back"
    - "convolutional 2d knowledge graph embeddings"
    - "lifted rule injection for relation embeddings"
    - "complex embeddings for simple link prediction"
    - "type constrained representation learning in knowledge graphs"

Connected through hyperedge HE-6 (induced by source paper "rule guided compositional representation learning on knowledge graphs"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "convolutional 2d knowledge graph embeddings"
    - "harnessing deep neural networks with logic rules"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "complex embeddings for simple link prediction"
    - "type constrained representation learning in knowledge graphs"
    - "modeling relation paths for representation learning of knowledge bases"

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"
    - "from one point to a manifold knowledge graph embedding for precise link prediction"

Connected through hyperedge HE-8 (induced by source paper "representation learning with ordered relation paths for knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "an interpretable knowledge transfer model for knowledge base completion"
    - "analogical inference for multi relational embeddings"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "proje embedding projection for knowledge graph completion"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"
    - "holographic embeddings of knowledge graphs"

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)