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: "is second order information helpful for large scale visual recognition"
  Abstract: By stacking layers of convolution and nonlinearity, convolutional networks (ConvNets) effectively learn from low-level to high-level features and discriminative representations. Since the end goal of large-scale recognition is to delineate complex boundaries of thousands of classes, adequate exploration of feature distributions is important for realizing full potentials of ConvNets. However, state-of-the-art works concentrate only on deeper or wider architecture design, while rarely exploring feature statistics higher than first-order. We take a step towards addressing this problem. Our method consists in covariance pooling, instead of the most commonly used first-order pooling, of high-level convolutional features. The main challenges involved are robust covariance estimation given a small sample of large-dimensional features and usage of the manifold structure of covariance matrices. To address these challenges, we present a Matrix Power Normalized Covariance (MPN-COV) method. We develop forward and backward propagation formulas regarding the nonlinear matrix functions such that MPN-COV can be trained end-to-end. In addition, we analyze both qualitatively and quantitatively its advantage over the well-known Log-Euclidean metric. On the ImageNet 2012 validation set, by combining MPN-COV we achieve over 4%, 3% and 2.5% gains for AlexNet, VGG-M and VGG-16, respectively; integration of MPN-COV into 50-layer ResNet outperforms ResNet-101 and is comparable to ResNet-152. The source code will be available on the project page: this http URL

Connected through hyperedge HE-1 (induced by source paper "high order paired aspp networks for semantic segmenation"):
  with members:
    - "asymmetric non local neural networks for semantic segmentation"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "mixed high order attention network for person re identification"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "ccnet criss cross attention for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "deep heterogeneous hashing for face video retrieval"):
  with members:
    - "the do s and don ts for cnn based face verification"
    - "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"
    - "umdfaces an annotated face dataset for training deep networks"

Connected through hyperedge HE-3 (induced by source paper "second order non local attention networks for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "bag of tricks for image classification with convolutional neural networks"
    - "batch dropblock network for person re identification and beyond"
    - "harmonious attention network for person re identification"
    - "dropblock a regularization method for convolutional networks"
    - "a coarse to fine pyramidal model for person re identification via multi loss dynamic training"
    - "compact generalized non local network"

Connected through hyperedge HE-4 (induced by source paper "eca net efficient channel attention for deep convolutional neural networks"):
  with members:
    - "expectation maximization attention networks for semantic segmentation"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "attention augmented convolutional networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "ccnet criss cross attention for semantic segmentation"
    - "channelnets compact and efficient convolutional neural networks via channel wise convolutions"
    - "gather excite exploiting feature context in convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "fixing the train test resolution discrepancy"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "billion scale semi supervised learning for image classification"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "multigrain a unified image embedding for classes and instances"
    - "the herbarium challenge 2019 dataset"
    - "do better imagenet models transfer better"
    - "bag of tricks for image classification with convolutional neural networks"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-6 (induced by source paper "deep cnns meet global covariance pooling better representation and generalization"):
  with members:
    - "cbam convolutional block attention module"
    - "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"
    - "factorized bilinear models for image recognition"

Connected through hyperedge HE-7 (induced by source paper "wider face and pedestrian challenge 2018 methods and results"):
  with members:
    - "person search in videos with one portrait through visual and temporal links"
    - "group normalization"
    - "arcface additive angular margin loss for deep face recognition"
    - "ring loss convex feature normalization for face recognition"
    - "sface an efficient network for face detection in large scale variations"
    - "unifying identification and context learning for person recognition"
    - "soft nms improving object detection with one line of code"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-8 (induced by source paper "deepsquare boosting the learning power of deep convolutional neural networks with elementwise square operators"):
  with members:
    - "global second order pooling convolutional networks"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "neural networks should be wide enough to learn disconnected decision regions"
    - "gather excite exploiting feature context in convolutional neural 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)