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: "sequential attention gan for interactive image editing via dialogue"
  Abstract: We introduce a new task - Interactive Image Editing via conversational language, where users can guide an agent to edit images via multi-turn dialogue. In each dialogue turn, the agent takes a source image and a natural language description as the input, and generates a modified image following the textual description. Two new datasets are introduced for this task (Zap-Seq and DeepFashion-Seq), which contain multi-turn dialog sessions with crowdsourced image-description sequences. The main challenges in this sequential and interactive image generation task are two-fold: 1) contextual consistency between a generated image and the given textual description; 2) step-by-step region-level modification to maintain visual consistency across the image sequence. To address these challenges, we propose a novel Sequential Attention Generative Adversarial Network (SeqAttnGAN) framework, which applies a neural state tracker to encode the previous image and the textual description in each dialogue turn, and uses a GAN framework to generate a modified version of the image that is consistent with the dialogue context and preceding images. To achieve better region-specific refinement, we also introduce a sequential attention mechanism into the model. Experiments on Zap-Seq and DeepFashion-Seq datasets show that the proposed SeqAttnGAN model outperforms state-of-the-art approaches on the interactive image editing task across all evaluation metrics on visual quality, image sequence coherence and text-image consistency.

Connected through hyperedge HE-1 (induced by source paper "tailorgan making user defined fashion designs"):
  with members:
    - "lip movements generation at a glance"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"
    - "viton an image based virtual try on network"
    - "texturegan controlling deep image synthesis with texture patches"
    - "a generative model of people in clothing"
    - "be your own prada fashion synthesis with structural coherence"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (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"
    - "learning to globally edit images with textual description"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "semantic object accuracy for generative text to image synthesis"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "interactive image generation using scene graphs"
    - "realistic image generation using region phrase attention"
    - "object driven text to image synthesis via adversarial training"
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "image synthesis from reconfigurable layout and style"
    - "visual relation conscious image generation from structured text"
    - "classification accuracy score for conditional generative models"

Connected through hyperedge HE-4 (induced by source paper "controlling style and semantics in weakly supervised image generation"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "lvis a dataset for large vocabulary instance segmentation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "chatpainter improving text to image generation using dialogue"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "tell draw and repeat generating and modifying images based on continual linguistic instruction"
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"

Connected through hyperedge HE-5 (induced by source paper "image manipulation with natural language using two sidedattentive conditional generative adversarial network"):
  with members:
    - "text adaptive generative adversarial networks manipulating images with natural language"
    - "progressive growing of gans for improved quality stability and variation"
    - "semantic image synthesis via adversarial learning"
    - "dualgan unsupervised dual learning for image to image translation"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"
    - "colorful image colorization"
    - "scribbler controlling deep image synthesis with sketch and color"

Connected through hyperedge HE-6 (induced by source paper "storygan a sequential conditional gan for story visualization"):
  with members:
    - "chatpainter improving text to image generation using dialogue"
    - "imagine this scripts to compositions to videos"
    - "learning to forecast and refine residual motion for image to video generation"
    - "every smile is unique landmark guided diverse smile generation"
    - "deep video generation prediction and completion of human action sequences"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "tell draw and repeat generating and modifying images based on continual linguistic instruction"
    - "probabilistic video generation using holistic attribute control"

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)