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 deep networks with structured layers by matrix backpropagation"
  Abstract: Deep neural network architectures have recently produced excellent results in a variety of areas in artificial intelligence and visual recognition, well surpassing traditional shallow architectures trained using hand-designed features. The power of deep networks stems both from their ability to perform local computations followed by pointwise non-linearities over increasingly larger receptive fields, and from the simplicity and scalability of the gradient-descent training procedure based on backpropagation. An open problem is the inclusion of layers that perform global, structured matrix computations like segmentation (e.g. normalized cuts) or higher-order pooling (e.g. log-tangent space metrics defined over the manifold of symmetric positive definite matrices) while preserving the validity and efficiency of an end-to-end deep training framework. In this paper we propose a sound mathematical apparatus to formally integrate global structured computation into deep computation architectures. At the heart of our methodology is the development of the theory and practice of backpropagation that generalizes to the calculus of adjoint matrix variations. The proposed matrix backpropagation methodology applies broadly to a variety of problems in machine learning or computational perception. Here we illustrate it by performing visual segmentation experiments using the BSDS and MSCOCO benchmarks, where we show that deep networks relying on second-order pooling and normalized cuts layers, trained end-to-end using matrix backpropagation, outperform counterparts that do not take advantage of such global layers.

Connected through hyperedge HE-1 (induced by source paper "deep heterogeneous hashing for face video retrieval"):
  with members:
    - "the do s and don ts for cnn based face verification"
    - "is second order information helpful for large scale visual recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "hashnet deep learning to hash by continuation"
    - "fast training of triplet based deep binary embedding networks"
    - "deep supervised hashing with triplet labels"
    - "deep cross modal hashing"
    - "a riemannian network for spd matrix learning"

Connected through hyperedge HE-2 (induced by source paper "cascaded projection end to end network compression and acceleration"):
  with members:
    - "learning to prune filters in convolutional neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "nvidia tensor core programmability performance precision"
    - "yolov3 an incremental improvement"
    - "discrimination aware channel pruning for deep neural networks"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "nisp pruning networks using neuron importance score propagation"
    - "an analysis of deep neural network models for practical applications"

Connected through hyperedge HE-3 (induced by source paper "switchable whitening for deep representation learning"):
  with members:
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "learning to adapt structured output space for semantic segmentation"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "group normalization"
    - "do normalization layers in a deep convnet really need to be distinct"
    - "differentiable learning to normalize via switchable normalization"
    - "batch instance normalization for adaptively style invariant neural networks"

Connected through hyperedge HE-4 (induced by source paper "deep cnns meet global covariance pooling better representation and generalization"):
  with members:
    - "cbam convolutional block attention module"
    - "is second order information helpful for large scale visual recognition"
    - "squeeze and excitation networks"
    - "improved bilinear pooling with cnns"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "towards effective codebookless model for image classification"
    - "aggregated residual transformations for deep neural networks"
    - "deep ten texture encoding network"

Connected through hyperedge HE-5 (induced by source paper "deepicp an end to end deep neural network for 3d point cloud registration"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "learning depth with convolutional spatial propagation network"
    - "deeptam deep tracking and mapping"
    - "efficient continuous time slam for 3d lidar based online mapping"
    - "deepmapping unsupervised map estimation from multiple point clouds"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "imls slam scan to model matching based on 3d data"

Connected through hyperedge HE-6 (induced by source paper "supervised fitting of geometric primitives to 3d point clouds"):
  with members:
    - "deep part induction from articulated object pairs"
    - "deep functional dictionaries learning consistent semantic structures on 3d models from functions"
    - "parsing geometry using structure aware shape templates"
    - "pcpnet learning local shape properties from raw point clouds"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "csgnet neural shape parser for constructive solid geometry"
    - "learning shape abstractions by assembling volumetric primitives"

Connected through hyperedge HE-7 (induced by source paper "scalable logo recognition using proxies"):
  with members:
    - "yolov3 an incremental improvement"
    - "arcface additive angular margin loss for deep face recognition"
    - "open logo detection challenge"
    - "focal loss for dense object detection"
    - "deep learning for logo recognition"
    - "squeeze and excitation networks"
    - "no fuss distance metric learning using proxies"
    - "open set logo detection and retrieval"

Connected through hyperedge HE-8 (induced by source paper "decorrelated batch normalization"):
  with members:
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"
    - "riemannian approach to batch normalization"
    - "projection based weight normalization for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "relative natural gradient for learning large complex models"

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)