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: "deep supervision with shape concepts for occlusion aware 3d object parsing"
  Abstract: Monocular 3D object parsing is highly desirable in various scenarios including occlusion reasoning and holistic scene interpretation. We present a deep convolutional neural network (CNN) architecture to localize semantic parts in 2D image and 3D space while inferring their visibility states, given a single RGB image. Our key insight is to exploit domain knowledge to regularize the network by deeply supervising its hidden layers, in order to sequentially infer intermediate concepts associated with the final task. To acquire training data in desired quantities with ground truth 3D shape and relevant concepts, we render 3D object CAD models to generate large-scale synthetic data and simulate challenging occlusion configurations between objects. We train the network only on synthetic data and demonstrate state-of-the-art performances on real image benchmarks including an extended version of KITTI, PASCAL VOC, PASCAL3D+ and IKEA for 2D and 3D keypoint localization and instance segmentation. The empirical results substantiate the utility of our deep supervision scheme by demonstrating effective transfer of knowledge from synthetic data to real images, resulting in less overfitting compared to standard end-to-end training.

Connected through hyperedge HE-1 (induced by source paper "multi task self supervised learning for human activity detection"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "deep learning for sensor based activity recognition a survey"
    - "unsupervised representation learning by predicting image rotations"
    - "universal language model fine tuning for text classification"
    - "protecting sensory data against sensitive inferences"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "audio visual scene analysis with self supervised multisensory features"

Connected through hyperedge HE-2 (induced by source paper "object parsing in sequences using coordconv gated recurrent networks"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "lstm pose machines"
    - "recurrent 3d pose sequence machines"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "warpnet weakly supervised matching for single view reconstruction"

Connected through hyperedge HE-3 (induced by source paper "car pose in context accurate pose estimation with ground plane constraints"):
  with members:
    - "a geometric approach to obtain a bird s eye view from an image"
    - "resolving 3d human pose ambiguities with 3d scene constraints"
    - "meta sim learning to generate synthetic datasets"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "a proposal based solution to spatio temporal action detection in untrimmed videos"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "groundnet monocular ground plane normal estimation with geometric consistency"

Connected through hyperedge HE-4 (induced by source paper "gs3d an efficient 3d object detection framework for autonomous driving"):
  with members:
    - "feature intertwiner for object detection"
    - "grid r cnn"
    - "acquisition of localization confidence for accurate object detection"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "recurrent scale approximation for object detection in cnn"

Connected through hyperedge HE-5 (induced by source paper "degeneracy in self calibration revisited and a deep learning solution for uncalibrated slam"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "baseline desensitizing in translation averaging"
    - "learning 2d to 3d lifting for object detection in 3d for autonomous vehicles"
    - "rolling shutter aware differential sfm and image rectification"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "deep supervision with intermediate concepts"
    - "fisheyerecnet a multi context collaborative deep network for fisheye image rectification"
    - "challenges in monocular visual odometry photometric calibration motion bias and rolling shutter effect"

Connected through hyperedge HE-6 (induced by source paper "gotta adapt em all joint pixel and feature level domain adaptation for recognition in the wild"):
  with members:
    - "towards high fidelity nonlinear 3d face morphable model"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "nonlinear 3d face morphable model"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "cycada cycle consistent adversarial domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "good semi supervised learning that requires a bad gan"
    - "focal loss for dense object detection"

Connected through hyperedge HE-7 (induced by source paper "hierarchical metric learning and matching for 2d and 3d geometric correspondences"):
  with members:
    - "deep supervision with intermediate concepts"
    - "the earth ain t flat monocular reconstruction of vehicles on steep and graded roads from a moving camera"
    - "learning spread out local feature descriptors"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "sfm net learning of structure and motion from video"
    - "semantic texture for robust dense tracking"

Connected through hyperedge HE-8 (induced by source paper "semantic part detection via matching learning to generalize to novel viewpoints from limited training data"):
  with members:
    - "acquisition of localization confidence for accurate object detection"
    - "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "learning non maximum suppression"
    - "squeeze and excitation networks"
    - "click here human localized keypoints as guidance for viewpoint estimation"
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "unpaired image to image translation using cycle consistent 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.CV(Computer Vision and Pattern Recognition)