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 intermediate concepts"
  Abstract: Recent data-driven approaches to scene interpretation predominantly pose inference as an end-to-end black-box mapping, commonly performed by a Convolutional Neural Network (CNN). However, decades of work on perceptual organization in both human and machine vision suggests that there are often intermediate representations that are intrinsic to an inference task, and which provide essential structure to improve generalization. In this work, we explore an approach for injecting prior domain structure into neural network training by supervising hidden layers of a CNN with intermediate concepts that normally are not observed in practice. We formulate a probabilistic framework which formalizes these notions and predicts improved generalization via this deep supervision method. One advantage of this approach is that we are able to train only from synthetic CAD renderings of cluttered scenes, where concept values can be extracted, but apply the results to real images. Our implementation achieves the state-of-the-art performance of 2D/3D keypoint localization and image classification on real image benchmarks, including KITTI, PASCAL VOC, PASCAL3D+, IKEA, and CIFAR100. We provide additional evidence that our approach outperforms alternative forms of supervision, such as multi-task networks.

Connected through hyperedge HE-1 (induced by source paper "deeply supervised knowledge synergy"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "knowledge transfer with jacobian matching"
    - "dropblock a regularization method for convolutional networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "deep co training for semi supervised image recognition"
    - "deeptravel a neural network based travel time estimation model with auxiliary supervision"
    - "modality distillation with multiple stream networks for action recognition"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-2 (induced by source paper "learning 2d to 3d lifting for object detection in 3d for autonomous vehicles"):
  with members:
    - "birdnet a 3d object detection framework from lidar information"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"
    - "toward multimodal image to image translation"
    - "improved adversarial systems for 3d object generation and reconstruction"

Connected through hyperedge HE-3 (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"
    - "fisheyerecnet a multi context collaborative deep network for fisheye image rectification"
    - "challenges in monocular visual odometry photometric calibration motion bias and rolling shutter effect"
    - "roomnet end to end room layout estimation"

Connected through hyperedge HE-4 (induced by source paper "hierarchical metric learning and matching for 2d and 3d geometric correspondences"):
  with members:
    - "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"
    - "lift learned invariant feature transform"

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)