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: "link prediction using embedded knowledge graphs"
  Abstract: Since large knowledge bases are typically incomplete, missing facts need to be inferred from observed facts in a task called knowledge base completion. The most successful approaches to this task have typically explored explicit paths through sequences of triples. These approaches have usually resorted to human-designed sampling procedures, since large knowledge graphs produce prohibitively large numbers of possible paths, most of which are uninformative. As an alternative approach, we propose performing a single, short sequence of interactive lookup operations on an embedded knowledge graph which has been trained through end-to-end backpropagation to be an optimized and compressed version of the initial knowledge base. Our proposed model, called Embedded Knowledge Graph Network (EKGN), achieves new state-of-the-art results on popular knowledge base completion benchmarks.

Connected through hyperedge HE-1 (induced by source paper "language based image editing with recurrent attentive models"):
  with members:
    - "stochastic answer networks for machine reading comprehension"
    - "reasonet learning to stop reading in machine comprehension"
    - "segmentation from natural language expressions"
    - "generative adversarial text to image synthesis"
    - "image to image translation with conditional adversarial networks"
    - "dynamic coattention networks for question answering"
    - "stackgan text to photo realistic image synthesis with stacked generative adversarial networks"
    - "machine comprehension using match lstm and answer pointer"

Connected through hyperedge HE-2 (induced by source paper "a novel embedding model for knowledge base completion based on convolutional neural network"):
  with members:
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "search personalization with embeddings"
    - "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"
    - "question answering over knowledge base with neural attention combining global knowledge information"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"

Connected through hyperedge HE-3 (induced by source paper "differentiable learning of logical rules for knowledge base reasoning"):
  with members:
    - "key value memory networks for directly reading documents"
    - "learning a natural language interface with neural programmer"
    - "learning to compose neural networks for question answering"
    - "words or characters fine grained gating for reading comprehension"
    - "gated attention readers for text comprehension"
    - "tensorlog a differentiable deductive database"
    - "neural programmer inducing latent programs with gradient descent"
    - "modeling relation paths for representation learning of knowledge bases"

Connected through hyperedge HE-4 (induced by source paper "an interpretable knowledge transfer model for knowledge base completion"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "neighborhood mixture model for knowledge base completion"
    - "lightrnn memory and computation efficient recurrent neural networks"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "transfer learning for low resource neural machine translation"
    - "sparse overcomplete word vector representations"
    - "traversing knowledge graphs in vector space"

Connected through hyperedge HE-5 (induced by source paper "scaffolding networks incremental learning and teaching through questioning"):
  with members:
    - "a deep compositional framework for human like language acquisition in virtual environment"
    - "learning language games through interaction"
    - "dialog based language learning"
    - "tracking the world state with recurrent entity networks"
    - "learning to compose neural networks for question answering"
    - "gated end to end memory networks"
    - "learning end to end goal oriented dialog"
    - "query reduction networks for question answering"

Connected through hyperedge HE-6 (induced by source paper "knowledge base completion baselines strike back"):
  with members:
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "discriminative gaifman models"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "proje embedding projection for knowledge graph completion"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

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)