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: "the informed sampler"
  Abstract: The informed sampler - a general inference technique for Bayesian posterior inference in generative models.This method leverages discriminative computer vision models for faster probabilistic inference in generative models.Three different applications that highlight common challenges of posterior inference.Detailed comparisons and analysis with respect to different baseline sampling based methods.Informed sampling is found to converge faster than all baseline samplers across diverse problems. Computer vision is hard because of a large variability in lighting, shape, and texture; in addition the image signal is non-additive due to occlusion. Generative models promised to account for this variability by accurately modelling the image formation process as a function of latent variables with prior beliefs. Bayesian posterior inference could then, in principle, explain the observation. While intuitively appealing, generative models for computer vision have largely failed to deliver on that promise due to the difficulty of posterior inference. As a result the community has favoured efficient discriminative approaches. We still believe in the usefulness of generative models in computer vision, but argue that we need to leverage existing discriminative or even heuristic computer vision methods. We implement this idea in a principled way with an informed sampler and in careful experiments demonstrate it on challenging generative models which contain renderer programs as their components. We concentrate on the problem of inverting an existing graphics rendering engine, an approach that can be understood as "Inverse Graphics". The informed sampler, using simple discriminative proposals based on existing computer vision technology, achieves significant improvements of inference.

Connected through hyperedge HE-1 (induced by source paper "a learned representation for scalable vector graphics"):
  with members:
    - "figr few shot image generation with reptile"
    - "tensor2tensor for neural machine translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "attention is all you need"
    - "film visual reasoning with a general conditioning layer"
    - "learning to infer graphics programs from hand drawn images"

Connected through hyperedge HE-2 (induced by source paper "a closest point proposal for mcmc based probabilistic surface registration"):
  with members:
    - "informed mcmc with bayesian neural networks for facial image analysis"
    - "iterative global similarity points a robust coarse to fine integration solution for pairwise 3d point cloud registration"
    - "morphable face models an open framework"
    - "point set registration coherent point drift"

Connected through hyperedge HE-3 (induced by source paper "inner space preserving generative pose machine"):
  with members:
    - "a generative model of people in clothing"
    - "cvae gan fine grained image generation through asymmetric training"
    - "learning from synthetic humans"
    - "towards large pose face frontalization in the wild"
    - "dualgan unsupervised dual learning for image to image translation"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "learning representations for automatic colorization"

Connected through hyperedge HE-4 (induced by source paper "synthesizing programs for images using reinforced adversarial learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "adversarial inverse graphics networks learning 2d to 3d lifting and image to image translation from unpaired supervision"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "csgnet neural shape parser for constructive solid geometry"
    - "learning human behaviors from motion capture by adversarial imitation"
    - "learning to infer graphics programs from hand drawn images"
    - "population based training of neural networks"

Connected through hyperedge HE-5 (induced by source paper "informed mcmc with bayesian neural networks for facial image analysis"):
  with members:
    - "inversefacenet deep single shot inverse face rendering from a single image"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "morphable face models an open framework"

Connected through hyperedge HE-6 (induced by source paper "attend infer repeat fast scene understanding with generative models"):
  with members:
    - "adaptive computation time for recurrent neural networks"
    - "salient object subitizing"
    - "spatial transformer networks"
    - "deep convolutional inverse graphics network"
    - "gradient estimation using stochastic computation graphs"
    - "efficient inference in occlusion aware generative models of images"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "draw a recurrent neural network for image generation"

Connected through hyperedge HE-7 (induced by source paper "deepcoder learning to write programs"):
  with members:
    - "deepmath deep sequence models for premise selection"
    - "adaptive neural compilation"
    - "gated graph sequence neural networks"
    - "programming with a differentiable forth interpreter"
    - "end to end memory networks"
    - "neural programmer inducing latent programs with gradient descent"
    - "learning simple algorithms from examples"
    - "learning program embeddings to propagate feedback on student code"

Connected through hyperedge HE-8 (induced by source paper "learning to superoptimize programs"):
  with members:
    - "adaptive neural compilation"
    - "learning to optimize"
    - "gradient estimation using stochastic computation graphs"
    - "adam a method for stochastic optimization"
    - "learning to discover efficient mathematical identities"

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)