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: "deep structured generative models"
  Abstract: Deep generative models have shown promising results in generating realistic images, but it is still non-trivial to generate images with complicated structures. The main reason is that most of the current generative models fail to explore the structures in the images including spatial layout and semantic relations between objects. To address this issue, we propose a novel deep structured generative model which boosts generative adversarial networks (GANs) with the aid of structure information. In particular, the layout or structure of the scene is encoded by a stochastic and-or graph (sAOG), in which the terminal nodes represent single objects and edges represent relations between objects. With the sAOG appropriately harnessed, our model can successfully capture the intrinsic structure in the scenes and generate images of complicated scenes accordingly. Furthermore, a detection network is introduced to infer scene structures from a image. Experimental results demonstrate the effectiveness of our proposed method on both modeling the intrinsic structures, and generating realistic images.

Connected through hyperedge HE-1 (induced by source paper "generating multiple objects at spatially distinct locations"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "chatpainter improving text to image generation using dialogue"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "yolov3 an incremental improvement"
    - "text2scene generating compositional scenes from textual descriptions"
    - "learning hierarchical semantic image manipulation through structured representations"
    - "image generation from scene graphs"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-2 (induced by source paper "multi objects generation with amortized structural regularization"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "multi object representation learning with iterative variational inference"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "image generation from scene graphs"
    - "graphical generative adversarial networks"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "progressive growing of gans for improved quality stability and variation"
    - "triple generative adversarial nets"

Connected through hyperedge HE-3 (induced by source paper "investigating object compositionality in generative adversarial networks"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "emergence of object segmentation in perturbed generative models"
    - "multi object representation learning with iterative variational inference"
    - "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"
    - "unsupervised object segmentation by redrawing"
    - "object discovery with a copy pasting gan"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "the gan landscape losses architectures regularization and normalization"

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.LG(Machine Learning)