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: "knowlege graph embedding by flexible translation"
  Abstract: Knowledge graph embedding refers to projecting entities and relations in knowledge graph into continuous vector spaces. State-of-the-art methods, such as TransE, TransH, and TransR build embeddings by treating relation as translation from head entity to tail entity. However, previous models can not deal with reflexive/one-to-many/many-to-one/many-to-many relations properly, or lack of scalability and efficiency. Thus, we propose a novel method, flexible translation, named TransF, to address the above issues. TransF regards relation as translation between head entity vector and tail entity vector with flexible magnitude. To evaluate the proposed model, we conduct link prediction and triple classification on benchmark datasets. Experimental results show that our method remarkably improve the performance compared with several state-of-the-art baselines.

Connected through hyperedge HE-1 (induced by source paper "transgcn coupling transformation assumptions with graph convolutional networks for link prediction"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "a comprehensive survey on graph neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "adaptive graph convolutional neural networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "embedding logical queries on knowledge graphs"
    - "large scale learnable graph convolutional networks"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "tucker tensor factorization for knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "hypernetwork knowledge graph embeddings"
    - "m walk learning to walk over graphs using monte carlo tree search"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "mutan multimodal tucker fusion for visual question answering"
    - "toruse knowledge graph embedding on a lie group"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "analogical inference for multi relational embeddings"

Connected through hyperedge HE-3 (induced by source paper "multi relational poincar e graph embeddings"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "riemannian adaptive optimization methods"
    - "hyperbolic neural networks"
    - "m walk learning to walk over graphs using monte carlo tree search"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "differentiable learning of logical rules for knowledge base reasoning"

Connected through hyperedge HE-4 (induced by source paper "relational memory based knowledge graph embedding"):
  with members:
    - "relational recurrent neural networks"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "attention is all you need"
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "convolutional 2d knowledge graph embeddings"
    - "neighborhood mixture model for knowledge base completion"
    - "complex embeddings for simple link prediction"
    - "from one point to a manifold knowledge graph embedding for precise link prediction"

Connected through hyperedge HE-5 (induced by source paper "drug drug interaction prediction based on knowledge graph embeddings and convolutional lstm network"):
  with members:
    - "detecting drug drug interactions using artificial neural networks and classic graph similarity measures"
    - "interaction embeddings for prediction and explanation in knowledge graphs"
    - "pytorch biggraph a large scale graph embedding system"
    - "hate speech detection a solved problem the challenging case of long tail on twitter"
    - "predicting rich drug drug interactions via biomedical knowledge graphs and text jointly embedding"
    - "analysis of the impact of negative sampling on link prediction in knowledge graphs"
    - "complex embeddings for simple link prediction"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

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.CL(Computation and Language)