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 feature to feature translator by alternating back propagation for zero shot learning"
  Abstract: We investigate learning feature-to-feature translator networks by alternating back-propagation as a general-purpose solution to zero-shot learning (ZSL) problems. Our method can be categorized to a generative model-based ZSL one. In contrast to the GAN or VAE that requires auxiliary networks to assist the training, our model consists of a single conditional generator that maps the class feature and a latent vector %accounting for randomness in the output to the image feature, and is trained by maximum likelihood estimation. The training process is a simple yet effective EM-like process that iterates the following two steps: (i) the inferential back-propagation to infer the latent noise vector of each observed data, and (ii) the learning back-propagation to update the parameters of the model. With slight modifications of our model, we also provide a solution to learning from incomplete visual features for ZSL. We conduct extensive comparisons with existing generative ZSL methods on five benchmarks, demonstrating the superiority of our method in not only performance but also convergence speed and computational cost. Specifically, our model outperforms the existing state-of-the-art methods by a remarkable margin up to $3.1\%$ and $4.0\%$ in ZSL and generalized ZSL settings, respectively.

Connected through hyperedge HE-1 (induced by source paper "oogan disentangling gan with one hot sampling and orthogonal regularization"):
  with members:
    - "infogan cr disentangling generative adversarial networks with contrastive regularizers"
    - "entropy and mutual information in models of deep neural networks"
    - "semantic guided multi attention localization for zero shot learning"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "spectral normalization for generative adversarial networks"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "isolating sources of disentanglement in variational autoencoders"
    - "a style based generator architecture for generative adversarial networks"

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)