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: "unsupervised image to sequence translation with canvas drawer networks"
  Abstract: Encoding images as a series of high-level constructs, such as brush strokes or discrete shapes, can often be key to both human and machine understanding. In many cases, however, data is only available in pixel form. We present a method for generating images directly in a high-level domain (e.g. brush strokes), without the need for real pairwise data. Specifically, we train a "canvas" network to imitate the mapping of high-level constructs to pixels, followed by a high-level "drawing" network which is optimized through this mapping towards solving a desired image recreation or translation task. We successfully discover sequential vector representations of symbols, large sketches, and 3D objects, utilizing only pixel data. We display applications of our method in image segmentation, and present several ablation studies comparing various configurations.

Connected through hyperedge HE-1 (induced by source paper "unsupervised doodling and painting with improved spiral"):
  with members:
    - "photo sketching inferring contour drawings from images"
    - "learning to paint with model based deep reinforcement learning"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "meta sim learning to generate synthetic datasets"
    - "pragmatic inference and visual abstraction enable contextual flexibility during visual communication"
    - "generating diverse programs with instruction conditioned reinforced adversarial learning"
    - "can computers create art"
    - "learning to search with mctsnets"

Connected through hyperedge HE-2 (induced by source paper "conv mpn convolutional message passing neural network for structured outdoor architecture reconstruction"):
  with members:
    - "floor sp inverse cad for floorplans by sequential room wise shortest path"
    - "ppgnet learning point pair graph for line segment detection"
    - "deep high resolution representation learning for human pose estimation"
    - "end to end wireframe parsing"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "spacenet a remote sensing dataset and challenge series"
    - "simple baselines for human pose estimation and tracking"
    - "monocular total capture posing face body and hands in the wild"

Connected through hyperedge HE-3 (induced by source paper "neural painters a learned differentiable constraint for generating brushstroke paintings"):
  with members:
    - "learning latent dynamics for planning from pixels"
    - "deep learning for classical japanese literature"
    - "recurrent world models facilitate policy evolution"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "improved training of wasserstein gans"
    - "neural style transfer a review"
    - "a neural algorithm of artistic style"

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)