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: "decoupled learning for conditional adversarial networks"
  Abstract: Incorporating encoding-decoding nets with adversarial nets has been widely adopted in image generation tasks. We observe that the state-of-the-art achievements were obtained by carefully balancing the reconstruction loss and adversarial loss, and such balance shifts with different network structures, datasets, and training strategies. Empirical studies have demonstrated that an inappropriate weight between the two losses may cause instability, and it is tricky to search for the optimal setting, especially when lacking prior knowledge on the data and network. #R##N#This paper gives the first attempt to relax the need of manual balancing by proposing the concept of \textit{decoupled learning}, where a novel network structure is designed that explicitly disentangles the backpropagation paths of the two losses. #R##N#Experimental results demonstrate the effectiveness, robustness, and generality of the proposed method. The other contribution of the paper is the design of a new evaluation metric to measure the image quality of generative models. We propose the so-called \textit{normalized relative discriminative score} (NRDS), which introduces the idea of relative comparison, rather than providing absolute estimates like existing metrics.

Connected through hyperedge HE-1 (induced by source paper "unsupervised end to end learning of discrete linguistic units for voice conversion"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "unsupervised speech representation learning using wavenet autoencoders"
    - "improved audio embeddings by adjacency based clustering with applications in spoken term detection"
    - "voice impersonation using generative adversarial networks"
    - "unsupervised learning of semantic audio representations"
    - "tacotron towards end to end speech synthesis"
    - "improved training of wasserstein gans"
    - "deep voice 3 2000 speaker neural text to speech"

Connected through hyperedge HE-2 (induced by source paper "pros and cons of gan evaluation measures"):
  with members:
    - "cross view image synthesis using conditional gans"
    - "generating adversarial examples with adversarial networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "gang of gans generative adversarial networks with maximum margin ranking"
    - "progressive growing of gans for improved quality stability and variation"
    - "sampling generative networks"
    - "statistics of deep generated images"

Connected through hyperedge HE-3 (induced by source paper "talking face generation by conditional recurrent adversarial network"):
  with members:
    - "image super resolution by neural texture transfer"
    - "video to video synthesis"
    - "lip movements generation at a glance"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "dual motion gan for future flow embedded video prediction"
    - "decomposing motion and content for natural video sequence prediction"
    - "r btn cross domain face composite and synthesis from limited facial patches"
    - "obamanet photo realistic lip sync from text"

Connected through hyperedge HE-4 (induced by source paper "towards quantitative methods to assess network generative models"):
  with members:
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "pros and cons of gan evaluation measures"
    - "graphrnn generating realistic graphs with deep auto regressive models"

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)