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: "understanding deep features with computer generated imagery"
  Abstract: We introduce an approach for analyzing the variation of features generated by convolutional neural networks (CNNs) with respect to scene factors that occur in natural images. Such factors may include object style, 3D viewpoint, color, and scene lighting configuration. Our approach analyzes CNN feature responses corresponding to different scene factors by controlling for them via rendering using a large database of 3D CAD models. The rendered images are presented to a trained CNN and responses for different layers are studied with respect to the input scene factors. We perform a decomposition of the responses based on knowledge of the input scene factors and analyze the resulting components. In particular, we quantify their relative importance in the CNN responses and visualize them using principal component analysis. We show qualitative and quantitative results of our study on three CNNs trained on large image datasets: AlexNet, Places, and Oxford VGG. We observe important differences across the networks and CNN layers for different scene factors and object categories. Finally, we demonstrate that our analysis based on computer-generated imagery translates to the network representation of natural images.

Connected through hyperedge HE-1 (induced by source paper "interpretable cnns for object classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "visual interpretability for deep learning a survey"
    - "explaining neural networks semantically and quantitatively"
    - "network dissection quantifying interpretability of deep visual representations"
    - "interactively transferring cnn patterns for part localization"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"
    - "visual explanations for convolutional neural networks via input resampling"

Connected through hyperedge HE-2 (induced by source paper "exploring visual patterns in projected human and machine decision making paths"):
  with members:
    - "confusionflow a model agnostic visualization for temporal analysis of classifier confusion"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-3 (induced by source paper "generating human action videos by coupling 3d game engines and probabilistic graphical models"):
  with members:
    - "pose guided human video generation"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "skeleton aided articulated motion generation"
    - "attentive semantic video generation using captions"
    - "carla an open urban driving simulator"
    - "temporal segment networks for action recognition in videos"

Connected through hyperedge HE-4 (induced by source paper "making convolutional networks shift invariant again"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "using pre training can improve model robustness and uncertainty"
    - "spatially transformed adversarial examples"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"
    - "pooling is neither necessary nor sufficient for appropriate deformation stability in cnns"
    - "why do deep convolutional networks generalize so poorly to small image transformations"

Connected through hyperedge HE-5 (induced by source paper "location field descriptors single image 3d model retrieval in the wild"):
  with members:
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "triplet center loss for multi view 3d object retrieval"
    - "3d pose estimation for fine grained object categories"
    - "learning shape priors for single view 3d completion and reconstruction"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-6 (induced by source paper "dynamic neural network decoupling"):
  with members:
    - "yolov3 an incremental improvement"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "you look twice gaternet for dynamic filter selection in cnns"
    - "dynamic channel pruning feature boosting and suppression"
    - "skipnet learning dynamic routing in convolutional networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "learning efficient convolutional networks through network slimming"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"

Connected through hyperedge HE-7 (induced by source paper "learning to estimate pose and shape of hand held objects from rgb images"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "scaling egocentric vision the epic kitchens dataset"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "global search with bernoulli alternation kernel for task oriented grasping informed by simulation"
    - "learning to estimate 3d hand pose from single rgb images"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-8 (induced by source paper "identity preserve transform understand what activity classification models have learnt"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "videos as space time region graphs"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "adversarial attacks beyond the image space"
    - "mask r cnn"
    - "temporal relational reasoning in videos"

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)