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: "learning to globally edit images with textual description"
  Abstract: We show how we can globally edit images using textual instructions: given a source image and a textual instruction for the edit, generate a new image transformed under this instruction. To tackle this novel problem, we develop three different trainable models based on RNN and Generative Adversarial Network (GAN). The models (bucket, filter bank, and end-to-end) differ in how much expert knowledge is encoded, with the most general version being purely end-to-end. To train these systems, we use Amazon Mechanical Turk to collect textual descriptions for around 2000 image pairs sampled from several datasets. Experimental results evaluated on our dataset validate our approaches. In addition, given that the filter bank model is a good compromise between generality and performance, we investigate it further by replacing RNN with Graph RNN, and show that Graph RNN improves performance. To the best of our knowledge, this is the first computational photography work on global image editing that is purely based on free-form textual instructions.

Connected through hyperedge HE-1 (induced by source paper "adjusting image attributes of localized regions with low level dialogue"):
  with members:
    - "a multimodal dialogue system for conversational image editing"
    - "expressing visual relationships via language"
    - "contextual slot carryover for disparate schemas"
    - "learning to describe differences between pairs of similar images"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "a system for automated image editing from natural language commands"
    - "mattnet modular attention network for referring expression comprehension"
    - "sequential attention gan for interactive image editing via dialogue"

Connected through hyperedge HE-2 (induced by source paper "expressing visual relationships via language"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "discriminability objective for training descriptive captions"
    - "learning to describe differences between pairs of similar images"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "learning to color from language"
    - "pragmatically informative image captioning with character level reference"
    - "reinforced video captioning with entailment rewards"
    - "improved image captioning via policy gradient optimization of spider"

Connected through hyperedge HE-3 (induced by source paper "sequential attention gan for interactive image editing via dialogue"):
  with members:
    - "text adaptive generative adversarial networks manipulating images with natural language"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "neural approaches to conversational ai"
    - "the neural painter multi turn image generation"
    - "dialog based interactive image retrieval"
    - "storygan a sequential conditional gan for story visualization"
    - "image grounded conversations multimodal context for natural question and response generation"
    - "language based image editing with recurrent attentive models"

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.CV(Computer Vision and Pattern Recognition)