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: "label refinery improving imagenet classification through label progression"
  Abstract: Among the three main components (data, labels, and models) of any supervised learning system, data and models have been the main subjects of active research. However, studying labels and their properties has received very little attention. Current principles and paradigms of labeling impose several challenges to machine learning algorithms. Labels are often incomplete, ambiguous, and redundant. In this paper we study the effects of various properties of labels and introduce the Label Refinery: an iterative procedure that updates the ground truth labels after examining the entire dataset. We show significant gain using refined labels across a wide range of models. Using a Label Refinery improves the state-of-the-art top-1 accuracy of (1) AlexNet from 59.3 to 67.2, (2) MobileNet from 70.6 to 73.39, (3) MobileNet-0.25 from 50.6 to 55.59, (4) VGG19 from 72.7 to 75.46, and (5) Darknet19 from 72.9 to 74.47.

Connected through hyperedge HE-1 (induced by source paper "self adaptive training beyond empirical risk minimization"):
  with members:
    - "deep gamblers learning to abstain with portfolio theory"
    - "symmetric cross entropy for robust learning with noisy labels"
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "deep double descent where bigger models and more data hurt"
    - "self learning to filter noisy labels with self ensembling"
    - "selectivenet a deep neural network with an integrated reject option"
    - "theoretically principled trade off between robustness and accuracy"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"

Connected through hyperedge HE-2 (induced by source paper "subclass distillation"):
  with members:
    - "relational knowledge distillation"
    - "momentum contrast for unsupervised visual representation learning"
    - "variational information distillation for knowledge transfer"
    - "contrastive representation distillation"
    - "patient knowledge distillation for bert model compression"
    - "similarity preserving knowledge distillation"
    - "self training with noisy student improves imagenet classification"
    - "paraphrasing complex network network compression via factor transfer"

Connected through hyperedge HE-3 (induced by source paper "highlight every step knowledge distillation via collaborative teaching"):
  with members:
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "pcc net perspective crowd counting via spatial convolutional network"
    - "model compression via distillation and quantization"
    - "private model compression via knowledge distillation"
    - "amalgamating knowledge towards comprehensive classification"
    - "improving fast segmentation with teacher student learning"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "progressive neural architecture search"

Connected through hyperedge HE-4 (induced by source paper "adversarial based knowledge distillation for multi model ensemble and noisy data refinement"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "relational knowledge distillation"
    - "towards instance level image to image translation"
    - "snapshot distillation teacher student optimization in one generation"
    - "knowledge distillation by on the fly native ensemble"
    - "multimodal unsupervised image to image translation"
    - "large scale distributed neural network training through online distillation"
    - "meal multi model ensemble via adversarial learning"

Connected through hyperedge HE-5 (induced by source paper "confidence regularized self training"):
  with members:
    - "unsupervised domain adaptation via calibrating uncertainties"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "open set domain adaptation for image and action recognition"
    - "conservative wasserstein training for pose estimation"
    - "unsupervised visual domain adaptation a deep max margin gaussian process approach"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"

Connected through hyperedge HE-6 (induced by source paper "min entropy latent model for weakly supervised object detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "deep learning for generic object detection a survey"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "deep self taught learning for weakly supervised object localization"
    - "two phase learning for weakly supervised object localization"
    - "weakly supervised object localization using things and stuff transfer"

Connected through hyperedge HE-7 (induced by source paper "be your own teacher improve the performance of convolutional neural networks via self distillation"):
  with members:
    - "slimmable neural networks"
    - "ktan knowledge transfer adversarial network"
    - "stochastic downsampling for cost adjustable inference and improved regularization in convolutional networks"
    - "meal multi model ensemble via adversarial learning"
    - "skipnet learning dynamic routing in convolutional networks"
    - "focal loss for dense object detection"
    - "deep mutual learning"
    - "blockdrop dynamic inference paths in residual networks"

Connected through hyperedge HE-8 (induced by source paper "synetgy algorithm hardware co design for convnet accelerators on embedded fpgas"):
  with members:
    - "co design of deep neural nets and neural net accelerators for embedded vision applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "shift based primitives for efficient convolutional neural networks"
    - "finn r an end to end deep learning framework for fast exploration of quantized neural networks"
    - "pact parameterized clipping activation for quantized 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)