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: "transg a generative mixture model for knowledge graph embedding"
  Abstract: Recently, knowledge graph embedding, which projects symbolic entities and relations into continuous vector space, has become a new, hot topic in artificial intelligence. This paper addresses a new issue of multiple relation semantics that a relation may have multiple meanings revealed by the entity pairs associated with the corresponding triples, and proposes a novel Gaussian mixture model for embedding, TransG. The new model can discover latent semantics for a relation and leverage a mixture of relation component vectors for embedding a fact triple. To the best of our knowledge, this is the first generative model for knowledge graph embedding, which is able to deal with multiple relation semantics. Extensive experiments show that the proposed model achieves substantial improvements against the state-of-the-art baselines.

Connected through hyperedge HE-1 (induced by source paper "augmenting neural machine translation with knowledge graphs"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "learning beyond datasets knowledge graph augmented neural networks for natural language processing"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "machine translation using semantic web technologies a survey"
    - "an evaluation of neural machine translation models on historical spelling normalization"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "learning word vectors for 157 languages"
    - "the importance of being recurrent for modeling hierarchical structure"

Connected through hyperedge HE-2 (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-3 (induced by source paper "learning beyond datasets knowledge graph augmented neural networks for natural language processing"):
  with members:
    - "machine learning with world knowledge the position and survey"
    - "lstm a search space odyssey"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "deep hashing a joint approach for image signature learning"
    - "a survey of inductive biases for factorial representation learning"
    - "the more you know using knowledge graphs for image classification"
    - "proje embedding projection for knowledge graph completion"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "expeditious generation of knowledge graph embeddings"):
  with members:
    - "neural information retrieval a literature review"
    - "modeling relation paths for representation learning of knowledge bases"
    - "locally adaptive translation for knowledge graph embedding"
    - "holographic embeddings of knowledge graphs"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "on multi relational link prediction with bilinear models"):
  with members:
    - "complex and holographic embeddings of knowledge graphs a comparison"
    - "analogical inference for multi relational embeddings"
    - "discriminative gaifman models"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"
    - "holographic embeddings of knowledge graphs"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "revisiting simple neural networks for learning representations of knowledge graphs"):
  with members:
    - "complex embeddings for simple link prediction"
    - "transa an adaptive approach for knowledge graph embedding"
    - "holographic embeddings of knowledge graphs"
    - "adam a method for stochastic optimization"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-7 (induced by source paper "inducing interpretability in knowledge graph embeddings"):
  with members:
    - "ksr a semantic representation of knowledge graph within a novel unsupervised paradigm"
    - "transa an adaptive approach for knowledge graph embedding"

Connected through hyperedge HE-8 (induced by source paper "entity embeddings with conceptual subspaces as a basis for plausible reasoning"):
  with members:
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "word representations via gaussian embedding"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"

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)