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: "an overview of embedding models of entities and relationships for knowledge base completion"
  Abstract: Knowledge bases (KBs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform knowledge base completion or link prediction, i.e., predict whether a relationship not in the knowledge base is likely to be true. This article serves as a brief overview of embedding models of entities and relationships for knowledge base completion, summarizing up-to-date experimental results on standard benchmark datasets FB15k, WN18, FB15k-237, WN18RR, FB13 and WN11.

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 "rdfframes knowledge graph access for machine learning tools"):
  with members:
    - "mapping rdf graphs to property graphs"
    - "on multi relational link prediction with bilinear models"
    - "g core a core for future graph query languages"
    - "convolutional 2d knowledge graph embeddings"
    - "semantics and complexity of sparql"

Connected through hyperedge HE-3 (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"
    - "improved knowledge graph embedding using background taxonomic information"
    - "how powerful are graph neural networks"
    - "learning dynamic embeddings from temporal interactions"
    - "learning sequence encoders for temporal knowledge graph completion"

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"
    - "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-5 (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"
    - "convolutional 2d knowledge graph embeddings"
    - "neighborhood mixture model for knowledge base completion"
    - "complex embeddings for simple link prediction"
    - "knowlege graph embedding by flexible translation"
    - "from one point to a manifold knowledge graph embedding for precise link prediction"

Connected through hyperedge HE-6 (induced by source paper "knowledge hypergraphs prediction beyond binary relations"):
  with members:
    - "hypernetwork knowledge graph embeddings"
    - "improved knowledge graph embedding using background taxonomic information"
    - "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"
    - "knowledge base completion baselines strike back"
    - "convolutional 2d knowledge graph embeddings"

Connected through hyperedge HE-7 (induced by source paper "secret semantically enhanced classification of real world tasks"):
  with members:
    - "muse modularizing unsupervised sense embeddings"
    - "cnn rnn a unified framework for multi label image classification"
    - "open question answering with weakly supervised embedding models"
    - "deep visual semantic alignments for generating image descriptions"
    - "zero shot learning through cross modal transfer"
    - "efficient estimation of word representations in vector space"
    - "word emdeddings through hellinger pca"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-8 (induced by source paper "unsupervised construction of knowledge graphs from text and code"):
  with members:
    - "nseen neural semantic embedding for entity normalization"
    - "mapping text to knowledge graph entities using multi sense lstms"
    - "a survey of machine learning for big code and naturalness"
    - "efficient estimation of word representations in vector space"

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)