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: "learning to generate chairs tables and cars with convolutional networks"
  Abstract: We train generative 'up-convolutional' neural networks which are able to generate images of objects given object style, viewpoint, and color. We train the networks on rendered 3D models of chairs, tables, and cars. Our experiments show that the networks do not merely learn all images by heart, but rather find a meaningful representation of 3D models allowing them to assess the similarity of different models, interpolate between given views to generate the missing ones, extrapolate views, and invent new objects not present in the training set by recombining training instances, or even two different object classes. Moreover, we show that such generative networks can be used to find correspondences between different objects from the dataset, outperforming existing approaches on this task.

Connected through hyperedge HE-1 (induced by source paper "tafssl task adaptive feature sub space learning for few shot classification"):
  with members:
    - "baby steps towards few shot learning with multiple semantics"
    - "multi level semantic feature augmentation for one shot learning"
    - "simpleshot revisiting nearest neighbor classification for few shot learning"
    - "meta learning with differentiable convex optimization"
    - "boosting few shot visual learning with self supervision"
    - "edge labeling graph neural network for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"
    - "laso label set operations networks for multi label few shot learning"

Connected through hyperedge HE-2 (induced by source paper "deep non line of sight reconstruction"):
  with members:
    - "non line of sight reconstruction using efficient transient rendering"
    - "occupancy networks learning 3d reconstruction in function space"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "non line of sight imaging with partial occluders and surface normals"
    - "learning representations and generative models for 3d point clouds"
    - "fast back projection for non line of sight reconstruction"
    - "convolutional pose machines"
    - "learning a predictable and generative vector representation for objects"

Connected through hyperedge HE-3 (induced by source paper "a novel online action detection framework from untrimmed video streams"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "temporal action detection with structured segment networks"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "unsupervised learning for physical interaction through video prediction"
    - "online action detection"
    - "online human action detection using joint classification regression recurrent neural networks"
    - "segmental spatiotemporal cnns for fine grained action segmentation"

Connected through hyperedge HE-4 (induced by source paper "end to end pixel based deep active inference for body perception and action"):
  with members:
    - "deep active inference as variational policy gradients"
    - "sensorimotor learning for artificial body perception"
    - "active inference body perception and action for humanoid robots"
    - "scaling active inference"
    - "multimodal representation models for prediction and control from partial information"
    - "visual reinforcement learning with imagined goals"
    - "probabilistic articulated real time tracking for robot manipulation"
    - "revisiting active perception"

Connected through hyperedge HE-5 (induced by source paper "metadapt meta learned task adaptive architecture for few shot classification"):
  with members:
    - "baby steps towards few shot learning with multiple semantics"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "few example object detection with model communication"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "laso label set operations networks for multi label few shot learning"

Connected through hyperedge HE-6 (induced by source paper "local class specific and global image level generative adversarial networks for semantic guided scene generation"):
  with members:
    - "seeing what a gan cannot generate"
    - "progressive pose attention transfer for person image generation"
    - "a layer based sequential framework for scene generation with gans"
    - "bridging the domain gap for ground to aerial image matching"
    - "singan learning a generative model from a single natural image"
    - "autogan neural architecture search for generative adversarial networks"
    - "coco gan generation by parts via conditional coordinating"
    - "few shot unsupervised image to image translation"

Connected through hyperedge HE-7 (induced by source paper "monocular neural image based rendering with continuous view control"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "transformable bottleneck networks"
    - "deepview view synthesis with learned gradient descent"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "pushing the boundaries of view extrapolation with multiplane images"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "visual object networks image generation with disentangled 3d representation"

Connected through hyperedge HE-8 (induced by source paper "hologan unsupervised learning of 3d representations from natural images"):
  with members:
    - "representation learning by rotating your faces"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "visual object networks image generation with disentangled 3d representation"
    - "learning category specific mesh reconstruction from image collections"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "spectral normalization for generative adversarial networks"
    - "towards open set identity preserving face synthesis"

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)