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: "oogan disentangling gan with one hot sampling and orthogonal regularization"
  Abstract: Exploring the potential of GANs for unsupervised disentanglement learning, this paper proposes a novel framework called OOGAN. While previous work mostly attempts to tackle disentanglement learning through VAE and seeks to minimize the Total Correlation (TC) objective with various sorts of approximation methods, we show that GANs have a natural advantage in disentangling with a straightforward latent variable sampling method. Furthermore, we provide a brand-new perspective on designing the structure of the generator and discriminator, demonstrating that a minor structural change and an orthogonal regularization on model weights entails improved disentanglement learning. Our experiments on several visual datasets confirm the effectiveness and superiority of this approach.

Connected through hyperedge HE-1 (induced by source paper "high fidelity synthesis with disentangled representation"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "weakly supervised disentanglement by pairwise similarities"
    - "infogan cr disentangling generative adversarial networks with contrastive regularizers"
    - "learning disentangled representations with reference based variational autoencoders"
    - "disentangling factors of variation using few labels"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "on the fairness of disentangled representations"
    - "flexibly fair representation learning by disentanglement"

Connected through hyperedge HE-2 (induced by source paper "unsupervised discovery of interpretable directions in the gan latent space"):
  with members:
    - "high fidelity synthesis with disentangled representation"
    - "salient object detection in the deep learning era an in depth survey"
    - "semantic hierarchy emerges in the deep generative representations for scene synthesis"
    - "on the steerability of generative adversarial networks"
    - "rpgan gans interpretability via random routing"
    - "interpreting the latent space of gans for semantic face editing"
    - "video to video synthesis"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "absent cross lingual sentence representation mapping with bidirectional gans"):
  with members:
    - "reinforcement knowledge graph reasoning for explainable recommendation"
    - "improving multilingual sentence embedding using bi directional dual encoder with additive margin softmax"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "characterizing departures from linearity in word translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning joint multilingual sentence representations with neural machine translation"
    - "triangle 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)