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: "csgnet neural shape parser for constructive solid geometry"
  Abstract: We present a neural architecture that takes as input a 2D or 3D shape and induces a program to generate it. The in- structions in our program are based on constructive solid geometry principles, i.e., a set of boolean operations on shape primitives defined recursively. Bottom-up techniques for this task that rely on primitive detection are inherently slow since the search space over possible primitive combi- nations is large. In contrast, our model uses a recurrent neural network conditioned on the input shape to produce a sequence of instructions in a top-down manner and is sig- nificantly faster. It is also more effective as a shape detec- tor than existing state-of-the-art detection techniques. We also demonstrate that our network can be trained on novel datasets without ground-truth program annotations through policy gradient techniques.

Connected through hyperedge HE-1 (induced by source paper "unsupervised program synthesis for images using tree structured lstm"):
  with members:
    - "learning to paint with model based deep reinforcement learning"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "learning to infer and execute 3d shape programs"
    - "write execute assess program synthesis with a repl"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "3d aware scene manipulation via inverse graphics"
    - "a tree based decoder for neural machine translation"
    - "automatic program synthesis of long programs with a learned garbage collector"

Connected through hyperedge HE-2 (induced by source paper "cvxnets learnable convex decomposition"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "interactive differentiable simulation"
    - "what do single view 3d reconstruction networks learn"
    - "attentive context normalization for robust permutation equivariant learning"
    - "bae net branched autoencoder for shape co segmentation"
    - "superquadrics revisited learning 3d shape parsing beyond cuboids"

Connected through hyperedge HE-3 (induced by source paper "learning to generate synthetic 3d training data through hybrid gradient"):
  with members:
    - "unrealrox an extremely photorealistic virtual reality environment for robotics simulations and synthetic data generation"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "gibson env real world perception for embodied agents"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "probabilistic model agnostic meta learning"

Connected through hyperedge HE-4 (induced by source paper "learning shape templates with structured implicit functions"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "supervised fitting of geometric primitives to 3d point clouds"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "learning category specific mesh reconstruction from image collections"
    - "learning implicit fields for generative shape modeling"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "parsing geometry using structure aware shape templates"
    - "occupancy networks learning 3d reconstruction in function space"

Connected through hyperedge HE-5 (induced by source paper "deep structured implicit functions"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "mesh r cnn"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "superquadrics revisited learning 3d shape parsing beyond cuboids"

Connected through hyperedge HE-6 (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"
    - "learning to infer graphics programs from hand drawn images"

Connected through hyperedge HE-7 (induced by source paper "superquadrics revisited learning 3d shape parsing beyond cuboids"):
  with members:
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "deepmvs learning multi view stereopsis"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-8 (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"

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)