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: "neurally guided procedural models amortized inference for procedural graphics programs using neural networks"
  Abstract: Probabilistic inference algorithms such as Sequential Monte Carlo (SMC) provide powerful tools for constraining procedural models in computer graphics, but they require many samples to produce desirable results. In this paper, we show how to create procedural models which learn how to satisfy constraints. We augment procedural models with neural networks which control how the model makes random choices based on the output it has generated thus far. We call such models neurally-guided procedural models. As a pre-computation, we train these models to maximize the likelihood of example outputs generated via SMC. They are then used as efficient SMC importance samplers, generating high-quality results with very few samples. We evaluate our method on L-system-like models with image-based constraints. Given a desired quality threshold, neurally-guided models can generate satisfactory results up to 10x faster than unguided models.

Connected through hyperedge HE-1 (induced by source paper "conv mpn convolutional message passing neural network for structured outdoor architecture reconstruction"):
  with members:
    - "floor sp inverse cad for floorplans by sequential room wise shortest path"
    - "ppgnet learning point pair graph for line segment detection"
    - "deep high resolution representation learning for human pose estimation"
    - "end to end wireframe parsing"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "spacenet a remote sensing dataset and challenge series"
    - "simple baselines for human pose estimation and tracking"
    - "monocular total capture posing face body and hands in the wild"

Connected through hyperedge HE-2 (induced by source paper "neural shape parsers for constructive solid geometry"):
  with members:
    - "deepspline data driven reconstruction of parametric curves and surfaces"
    - "supervised fitting of geometric primitives to 3d point clouds"
    - "learning to infer and execute 3d shape programs"
    - "superquadrics revisited learning 3d shape parsing beyond cuboids"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "inferring and executing programs for visual reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "csgnet neural shape parser for constructive solid geometry"

Connected through hyperedge HE-3 (induced by source paper "learning to infer and execute 3d shape programs"):
  with members:
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "scores shape composition with recursive substructure priors"
    - "physical primitive decomposition"
    - "learning to adapt meta learning for model based control"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "robustfill neural program learning under noisy i o"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-4 (induced by source paper "neurally guided structure inference"):
  with members:
    - "learning to infer and execute 3d shape programs"
    - "automatic program synthesis of long programs with a learned garbage collector"
    - "robust text to sql generation with execution guided decoding"
    - "tree to tree neural networks for program translation"
    - "neural guided constraint logic programming for program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "making neural programming architectures generalize via recursion"
    - "neural sketch learning for conditional program generation"

Connected through hyperedge HE-5 (induced by source paper "meta learning mcmc proposals"):
  with members:
    - "learning to optimize neural nets"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "signal based bayesian seismic monitoring"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"
    - "inference compilation and universal probabilistic programming"
    - "importance weighted autoencoders"
    - "neural adaptive sequential monte carlo"

Connected through hyperedge HE-6 (induced by source paper "learning to generate the unseen via part synthesis and composition"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "learning part generation and assembly for structure aware shape synthesis"
    - "image inpainting for irregular holes using partial convolutions"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "scores shape composition with recursive substructure priors"
    - "compositional gan learning image conditional binary composition"
    - "generative image inpainting with contextual attention"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"

Connected through hyperedge HE-7 (induced by source paper "fast and flexible indoor scene synthesis via deep convolutional generative models"):
  with members:
    - "virtualhome simulating household activities via programs"
    - "deep generative modeling for scene synthesis via hybrid representations"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "grains generative recursive autoencoders for indoor scenes"
    - "chalet cornell house agent learning environment"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "minos multimodal indoor simulator for navigation in complex environments"

Connected through hyperedge HE-8 (induced by source paper "componet learning to generate the unseen by part synthesis and composition"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "learning part generation and assembly for structure aware shape synthesis"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "compositional gan learning conditional image composition"
    - "scores shape composition with recursive substructure priors"
    - "generative image inpainting with contextual attention"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "began boundary equilibrium 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.GR(Graphics)