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: "disentangling multiple conditional inputs in gans"
  Abstract: In this paper, we propose a method that disentangles the effects of multiple input conditions in Generative Adversarial Networks (GANs). In particular, we demonstrate our method in controlling color, texture, and shape of a generated garment image for computer-aided fashion design. To disentangle the effect of input attributes, we customize conditional GANs with consistency loss functions. In our experiments, we tune one input at a time and show that we can guide our network to generate novel and realistic images of clothing articles. In addition, we present a fashion design process that estimates the input attributes of an existing garment and modifies them using our generator.

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"
    - "language guided fashion image manipulation with feature wise transformations"

Connected through hyperedge HE-2 (induced by source paper "attention based fusion for multi source human image generation"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "soft gated warping gan for pose guided person image synthesis"
    - "synthesizing images of humans in unseen poses"
    - "mc gan multi conditional generative adversarial network for image synthesis"
    - "the perception distortion tradeoff"
    - "a variational u net for conditional appearance and shape generation"
    - "dense pose transfer"
    - "dynamic few shot visual learning without forgetting"

Connected through hyperedge HE-3 (induced by source paper "mask embedding in conditional gan for guided synthesis of high resolution images"):
  with members:
    - "matching thermal to visible face images using a semantic guided generative adversarial network"
    - "singan learning a generative model from a single natural image"
    - "image synthesis in multi contrast mri with conditional generative adversarial networks"
    - "high resolution mammogram synthesis using progressive generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "high resolution deep convolutional generative adversarial networks"
    - "face aging with conditional generative adversarial networks"
    - "improved training of wasserstein gans"

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)