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: "multigrain a unified image embedding for classes and instances"
  Abstract: MultiGrain is a network architecture producing compact vector representations that are suited both for image classification and particular object retrieval. It builds on a standard classification trunk. The top of the network produces an embedding containing coarse and fine-grained information, so that images can be recognized based on the object class, particular object, or if they are distorted copies. Our joint training is simple: we minimize a cross-entropy loss for classification and a ranking loss that determines if two images are identical up to data augmentation, with no need for additional labels. A key component of MultiGrain is a pooling layer that takes advantage of high-resolution images with a network trained at a lower resolution. #R##N#When fed to a linear classifier, the learned embeddings provide state-of-the-art classification accuracy. For instance, we obtain 79.4% top-1 accuracy with a ResNet-50 learned on Imagenet, which is a +1.8% absolute improvement over the AutoAugment method. When compared with the cosine similarity, the same embeddings perform on par with the state-of-the-art for image retrieval at moderate resolutions.

Connected through hyperedge HE-1 (induced by source paper "class feature pyramids for video explanation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "video classification with channel separated convolutional networks"
    - "learning spatio temporal representation with local and global diffusion"
    - "saliency tubes visual explanations for spatio temporal convolutions"
    - "large scale weakly supervised pre training for video action recognition"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "grounding visual explanations"
    - "multi fiber networks for video recognition"

Connected through hyperedge HE-2 (induced by source paper "mix match training convnets with mixed image sizes for improved accuracy speed and scale resiliency"):
  with members:
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "faster neural network training with data echoing"
    - "fixing the train test resolution discrepancy"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "yolov3 an incremental improvement"
    - "regularized evolution for image classifier architecture search"
    - "autoaugment learning augmentation policies from data"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"

Connected through hyperedge HE-3 (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"
    - "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"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"

Connected through hyperedge HE-4 (induced by source paper "a benchmark on tricks for large scale image retrieval"):
  with members:
    - "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"
    - "semi supervised feature level attribute manipulation for fashion image retrieval"
    - "combination of multiple global descriptors for image retrieval"
    - "efficient nearest neighbors search for large scale landmark recognition"
    - "bag of tricks for image classification with convolutional neural networks"
    - "batch dropblock network for person re identification and beyond"
    - "efficient image retrieval via decoupling diffusion into online and offline processing"
    - "harmonious attention network for person re identification"

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)