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: "inverse graphics with probabilistic cad models"
  Abstract: Recently, multiple formulations of vision problems as probabilistic inversions of generative models based on computer graphics have been proposed. However, applications to 3D perception from natural images have focused on low-dimensional latent scenes, due to challenges in both modeling and inference. Accounting for the enormous variability in 3D object shape and 2D appearance via realistic generative models seems intractable, as does inverting even simple versions of the many-to-many computations that link 3D scenes to 2D images. This paper proposes and evaluates an approach that addresses key aspects of both these challenges. We show that it is possible to solve challenging, real-world 3D vision problems by approximate inference in generative models for images based on rendering the outputs of probabilistic CAD (PCAD) programs. Our PCAD object geometry priors generate deformable 3D meshes corresponding to plausible objects and apply affine transformations to place them in a scene. Image likelihoods are based on similarity in a feature space based on standard mid-level image representations from the vision literature. Our inference algorithm integrates single-site and locally blocked Metropolis-Hastings proposals, Hamiltonian Monte Carlo and discriminative data-driven proposals learned from training data generated from our models. We apply this approach to 3D human pose estimation and object shape reconstruction from single images, achieving quantitative and qualitative performance improvements over state-of-the-art baselines.

Connected through hyperedge HE-1 (induced by source paper "learning programmatically structured representations with perceptor gradients"):
  with members:
    - "towards symbolic reinforcement learning with common sense"
    - "isolating sources of disentanglement in variational autoencoders"
    - "programmatically interpretable reinforcement learning"
    - "deep learning for physical processes incorporating prior scientific knowledge"
    - "deep reinforcement learning a brief survey"
    - "learning to infer graphics programs from hand drawn images"
    - "using program induction to interpret transition system dynamics"
    - "beating atari with natural language guided reinforcement learning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "flexible neural representation for physics prediction"):
  with members:
    - "probing physics knowledge using tools from developmental psychology"
    - "intphys a framework and benchmark for visual intuitive physics reasoning"
    - "3d physnet learning the intuitive physics of non rigid object deformations"
    - "learning to play with intrinsically motivated self aware agents"
    - "visual interaction networks"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "geometric deep learning going beyond euclidean data"
    - "unsupervised learning for physical interaction through video prediction"

Connected through hyperedge HE-4 (induced by source paper "recent advances in zero shot recognition"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "joint intermodal and intramodal label transfers for extremely rare or unseen classes"
    - "progressive neural networks"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "zero shot learning the good the bad and the ugly"
    - "dynamic concept composition for zero example event detection"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"

Connected through hyperedge HE-5 (induced by source paper "disentangled representations in neural models"):
  with members:
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"
    - "importance weighted autoencoders"
    - "neural programmer inducing latent programs with gradient descent"
    - "learning simple algorithms from examples"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "spatial transformer networks"
    - "deep convolutional inverse graphics network"
    - "generating images from captions with attention"

Connected through hyperedge HE-6 (induced by source paper "a compositional object based approach to learning physical dynamics"):
  with members:
    - "unsupervised learning for physical interaction through video prediction"
    - "to fall or not to fall a visual approach to physical stability prediction"
    - "understanding visual concepts with continuation learning"
    - "gated graph sequence neural networks"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "learning to compose neural networks for question answering"
    - "building machines that learn and think like people"
    - "learning physical intuition of block towers by example"

Connected through hyperedge HE-7 (induced by source paper "unsupervised learning of 3d structure from images"):
  with members:
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "attend infer repeat fast scene understanding with generative models"
    - "variational inference for monte carlo objectives"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "view synthesis by appearance flow"
    - "importance weighted autoencoders"
    - "shapenet an information rich 3d model repository"
    - "multi view convolutional neural networks for 3d shape recognition"

Connected through hyperedge HE-8 (induced by source paper "generating holistic 3d scene abstractions for text based image retrieval"):
  with members:
    - "spice semantic propositional image caption evaluation"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "visual relationship detection with language priors"
    - "towards scene understanding with detailed 3d object representations"
    - "fast r cnn"
    - "very deep convolutional networks for large scale image recognition"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"
    - "distributed representations of words and phrases and their compositionality"

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)