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: "incorporating gan for negative sampling in knowledge representation learning"
  Abstract: Knowledge representation learning aims at modeling knowledge graph by encoding entities and relations into a low dimensional space. Most of the traditional works for knowledge embedding need negative sampling to minimize a margin-based ranking loss. However, those works construct negative samples through a random mode, by which the samples are often too trivial to fit the model efficiently. In this paper, we propose a novel knowledge representation learning framework based on Generative Adversarial Networks (GAN). In this GAN-based framework, we take advantage of a generator to obtain high-quality negative samples. Meanwhile, the discriminator in GAN learns the embeddings of the entities and relations in knowledge graph. Thus, we can incorporate the proposed GAN-based framework into various traditional models to improve the ability of knowledge representation learning. Experimental results show that our proposed GAN-based framework outperforms baselines on triplets classification and link prediction tasks.

Connected through hyperedge HE-1 (induced by source paper "efficiently embedding dynamic knowledge graphs"):
  with members:
    - "nscaching simple and efficient negative sampling for knowledge graph embedding"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "attributed network embedding for learning in a dynamic environment"
    - "convolutional 2d knowledge graph embeddings"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep convolutional networks on graph structured data"

Connected through hyperedge HE-2 (induced by source paper "composing knowledge graph embeddings via word embeddings"):
  with members:
    - "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"
    - "transg a generative mixture model for knowledge graph embedding"

Connected through hyperedge HE-3 (induced by source paper "evaluating the effectiveness of margin parameter when learning knowledge embedding representation for domain specific multi relational categorized data"):
  with members:
    - "knowledge base completion baselines strike back"
    - "complex embeddings for simple link prediction"
    - "transa an adaptive approach for knowledge graph embedding"
    - "holographic embeddings of knowledge graphs"

Connected through hyperedge HE-4 (induced by source paper "adversarial contrastive estimation"):
  with members:
    - "learning approximate inference networks for structured prediction"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "contrastive learning for image captioning"
    - "improved training of wasserstein gans"
    - "convolutional 2d knowledge graph embeddings"
    - "efficient softmax approximation for gpus"

Connected through hyperedge HE-5 (induced by source paper "nscaching simple and efficient negative sampling for knowledge graph embedding"):
  with members:
    - "on evaluating embedding models for knowledge base completion"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "analogical inference for multi relational embeddings"
    - "node2vec scalable feature learning for networks"
    - "complex embeddings for simple link prediction"
    - "from one point to a manifold knowledge graph embedding for precise link prediction"

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.AI(Artificial Intelligence)