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: "semantic sentence embeddings for paraphrasing and text summarization"
  Abstract: This paper introduces a sentence to vector encoding framework suitable for advanced natural language processing. Our latent representation is shown to encode sentences with common semantic information with similar vector representations. The vector representation is extracted from an encoder-decoder model which is trained on sentence paraphrase pairs. We demonstrate the application of the sentence representations for two different tasks -- sentence paraphrasing and paragraph summarization, making it attractive for commonly used recurrent frameworks that process text. Experimental results help gain insight how vector representations are suitable for advanced language embedding.

Connected through hyperedge HE-1 (induced by source paper "vector learning for cross domain representations"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "starspace embed all the things"
    - "textually customized video summaries"
    - "deep image retrieval learning global representations for image search"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-2 (induced by source paper "semantically invariant text to image generation"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "starspace embed all the things"
    - "progressive growing of gans for improved quality stability and variation"
    - "recurrent topic transition gan for visual paragraph generation"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "end to end learning of deep visual representations for image retrieval"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "yolo9000 better faster stronger"

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)