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 color from language"
  Abstract: Automatic colorization is the process of adding color to greyscale images. We condition this process on language, allowing end users to manipulate a colorized image by feeding in different captions. We present two different architectures for language-conditioned colorization, both of which produce more accurate and plausible colorizations than a language-agnostic version. Through this language-based framework, we can dramatically alter colorizations by manipulating descriptive color words in captions.

Connected through hyperedge HE-1 (induced by source paper "expressing visual relationships via language"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "learning to globally edit images with textual description"
    - "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"
    - "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-2 (induced by source paper "tag2pix line art colorization using text tag with secat and changing loss"):
  with members:
    - "user guided deep anime line art colorization with conditional adversarial networks"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "semantic image synthesis via adversarial learning"
    - "language based image editing with recurrent attentive models"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "comicolorization semi automatic manga colorization"
    - "towards the automatic anime characters creation with generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "bilinear representation for language based image editing using conditional generative adversarial networks"):
  with members:
    - "language guided fashion image manipulation with feature wise transformations"
    - "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-4 (induced by source paper "language guided fashion image manipulation with feature wise transformations"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "da gan instance level image translation by deep attention generative adversarial networks with supplementary materials"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "unsupervised person image synthesis in arbitrary poses"
    - "a variational u net for conditional appearance and shape generation"
    - "attention aware generative adversarial networks ata gans"
    - "viton an image based virtual try on network"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

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)