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: "language guided fashion image manipulation with feature wise transformations"
  Abstract: Developing techniques for editing an outfit image through natural sentences and accordingly generating new outfits has promising applications for art, fashion and design. However, it is considered as a certainly challenging task since image manipulation should be carried out only on the relevant parts of the image while keeping the remaining sections untouched. Moreover, this manipulation process should generate an image that is as realistic as possible. In this work, we propose FiLMedGAN, which leverages feature-wise linear modulation (FiLM) to relate and transform visual features with natural language representations without using extra spatial information. Our experiments demonstrate that this approach, when combined with skip connections and total variation regularization, produces more plausible results than the baseline work, and has a better localization capability when generating new outfits consistent with the target description.

Connected through hyperedge HE-1 (induced by source paper "compatible and diverse fashion image inpainting"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "learning type aware embeddings for fashion compatibility"
    - "design design inspiration from generative networks"
    - "instance level human parsing via part grouping network"
    - "toward characteristic preserving image based virtual try on network"
    - "multimodal unsupervised image to image translation"
    - "a variational u net for conditional appearance and shape generation"

Connected through hyperedge HE-2 (induced by source paper "bilinear representation for language based image editing using conditional generative adversarial networks"):
  with members:
    - "learning to color from language"
    - "semantic image synthesis via adversarial learning"
    - "language based image editing with recurrent attentive models"
    - "be your own prada fashion synthesis with structural coherence"
    - "film visual reasoning with a general conditioning layer"
    - "residual attention network for image classification"
    - "hadamard product for low rank bilinear pooling"
    - "low rank bilinear pooling for fine grained classification"

Connected through hyperedge HE-3 (induced by source paper "joint visual textual embedding for multimodal style search"):
  with members:
    - "deep cosine metric learning for person re identification"
    - "learning fashion compatibility with bidirectional lstms"
    - "amc attention guided multi modal correlation learning for image search"
    - "dual attention networks for multimodal reasoning and matching"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "deep fragment embeddings for bidirectional image sentence mapping"

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)