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: "generative adversarial image synthesis with decision tree latent controller"
  Abstract: This paper proposes the decision tree latent controller generative adversarial network (DTLC-GAN), an extension of a GAN that can learn hierarchically interpretable representations without relying on detailed supervision. To impose a hierarchical inclusion structure on latent variables, we incorporate a new architecture called the DTLC into the generator input. The DTLC has a multiple-layer tree structure in which the ON or OFF of the child node codes is controlled by the parent node codes. By using this architecture hierarchically, we can obtain the latent space in which the lower layer codes are selectively used depending on the higher layer ones. To make the latent codes capture salient semantic features of images in a hierarchically disentangled manner in the DTLC, we also propose a hierarchical conditional mutual information regularization and optimize it with a newly defined curriculum learning method that we propose as well. This makes it possible to discover hierarchically interpretable representations in a layer-by-layer manner on the basis of information gain by only using a single DTLC-GAN model. We evaluated the DTLC-GAN on various datasets, i.e., MNIST, CIFAR-10, Tiny ImageNet, 3D Faces, and CelebA, and confirmed that the DTLC-GAN can learn hierarchically interpretable representations with either unsupervised or weakly supervised settings. Furthermore, we applied the DTLC-GAN to image-retrieval tasks and showed its effectiveness in representation learning.

Connected through hyperedge HE-1 (induced by source paper "inducing optimal attribute representations for conditional gans"):
  with members:
    - "few shot adversarial learning of realistic neural talking head models"
    - "multi label image recognition with graph convolutional networks"
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "auglabel exploiting word representations to augment labels for face attribute classification"
    - "biphasic learning of gans for high resolution image to image translation"
    - "cgans with projection discriminator"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "graph neural networks a review of methods and applications"

Connected through hyperedge HE-2 (induced by source paper "deep tree learning for zero shot face anti spoofing"):
  with members:
    - "group normalization"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "visual question reasoning on general dependency tree"
    - "tree to tree neural networks for program translation"
    - "dense face alignment"
    - "learning a deep embedding model for zero shot learning"
    - "learn convolutional neural network for face anti spoofing"
    - "zero shot learning through cross modal transfer"

Connected through hyperedge HE-3 (induced by source paper "label noise robust generative adversarial networks"):
  with members:
    - "cgans with projection discriminator"
    - "video to video synthesis"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "how good is my gan"
    - "co sampling training robust networks for extremely noisy supervision"
    - "joint optimization framework for learning with noisy labels"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "noise2noise learning image restoration without clean data"

Connected through hyperedge HE-4 (induced by source paper "class distinct and class mutual image generation with gans"):
  with members:
    - "cgans with projection discriminator"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "smit stochastic multi label image to image translation"
    - "modular generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "which training methods for gans do actually converge"

Connected through hyperedge HE-5 (induced by source paper "an interpretable generative model for handwritten digit image synthesis"):
  with members:
    - "interpretable convolutional neural networks via feedforward design"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "on data driven saak transform"
    - "learning hierarchical features from generative models"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "lr gan layered recursive generative adversarial networks for image generation"
    - "improved training of wasserstein gans"
    - "generating images part by part with composite generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "conditional video generation using action appearance captions"):
  with members:
    - "pose guided human video generation"
    - "deep video generation prediction and completion of human action sequences"
    - "probabilistic video generation using holistic attribute control"
    - "to create what you tell generating videos from captions"
    - "attentive semantic video generation using captions"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "mocogan decomposing motion and content for video generation"
    - "temporal generative adversarial nets with singular value clipping"

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)