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: "training deeper convolutional networks with deep supervision"
  Abstract: One of the most promising ways of improving the performance of deep convolutional neural networks is by increasing the number of convolutional layers. However, adding layers makes training more difficult and computationally expensive. In order to train deeper networks, we propose to add auxiliary supervision branches after certain intermediate layers during training. We formulate a simple rule of thumb to determine where these branches should be added. The resulting deeply supervised structure makes the training much easier and also produces better classification results on ImageNet and the recently released, larger MIT Places dataset

Connected through hyperedge HE-1 (induced by source paper "attention guided chained context aggregation for semantic segmentation"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "selective kernel networks"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "auxiliary learning for deep multi task learning"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "deep attention based classification network for robust depth prediction"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "ocnet object context network for scene parsing"
    - "deep ordinal regression network for monocular depth estimation"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "discrimination aware channel pruning for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "depth completion via deep basis fitting"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "depth coefficients for depth completion"
    - "dense depth posterior ddp from single image and sparse range"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "voiced depth completion from inertial odometry and vision"
    - "noise aware unsupervised deep lidar stereo fusion"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"

Connected through hyperedge HE-4 (induced by source paper "residual cnds for grand challenge scene dataset"):
  with members:
    - "places an image database for deep scene understanding"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"

Connected through hyperedge HE-5 (induced by source paper "efficient 2 5d hand pose estimation via auxiliary multi task training for embedded devices"):
  with members:
    - "expressive body capture 3d hands face and body from a single image"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "monocular total capture posing face body and hands in the wild"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "ganerated hands for real time 3d hand tracking from monocular rgb"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"

Connected through hyperedge HE-6 (induced by source paper "the costar block stacking dataset learning with workspace constraints"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "monocular object orientation estimation using riemannian regression and classification networks"
    - "playing hard exploration games by watching youtube"
    - "a real time robotic grasp approach with oriented anchor box"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "shapestacks learning vision based physical intuition for generalised object stacking"

Connected through hyperedge HE-7 (induced by source paper "a deeper look at power normalizations"):
  with members:
    - "improved bilinear pooling with cnns"
    - "domain adaptation by mixture of alignments of second or higher order scatter tensors"
    - "a riemannian network for spd matrix learning"
    - "bilinear cnn models for fine grained visual recognition"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"
    - "places205 vggnet models for scene recognition"

Connected through hyperedge HE-8 (induced by source paper "point cloud based place recognition using cnn feature extraction"):
  with members:
    - "locally supervised deep hybrid model for scene recognition"
    - "deep learning in neural networks"
    - "learning deep features for discriminative localization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "caffe convolutional architecture for fast feature embedding"
    - "cnn features off the shelf an astounding baseline for recognition"

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)