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: "meal multi model ensemble via adversarial learning"
  Abstract: Often the best performing deep neural models are ensembles of multiple base-level networks. Unfortunately, the space required to store these many networks, and the time required to execute them at test-time, prohibits their use in applications where test sets are large (e.g., ImageNet). In this paper, we present a method for compressing large, complex trained ensembles into a single network, where knowledge from a variety of trained deep neural networks (DNNs) is distilled and transferred to a single DNN. In order to distill diverse knowledge from different trained (teacher) models, we propose to use adversarial-based learning strategy where we define a block-wise training loss to guide and optimize the predefined student network to recover the knowledge in teacher models, and to promote the discriminator network to distinguish teacher vs. student features simultaneously. The proposed ensemble method (MEAL) of transferring distilled knowledge with adversarial learning exhibits three important advantages: (1) the student network that learns the distilled knowledge with discriminators is optimized better than the original model; (2) fast inference is realized by a single forward pass, while the performance is even better than traditional ensembles from multi-original models; (3) the student network can learn the distilled knowledge from a teacher model that has arbitrary structures. Extensive experiments on CIFAR-10/100, SVHN and ImageNet datasets demonstrate the effectiveness of our MEAL method. On ImageNet, our ResNet-50 based MEAL achieves top-1/5 21.79%/5.99% val error, which outperforms the original model by 2.06%/1.14%. Code and models are available at: this https URL

Connected through hyperedge HE-1 (induced by source paper "reactnet towards precise binary neural network with generalized activation functions"):
  with members:
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "regularizing activation distribution for training binarized deep networks"
    - "accurate and compact convolutional neural networks with trained binarization"
    - "xnor net improved binary neural networks"
    - "dabnn a super fast inference framework for binary neural networks on arm devices"
    - "global sparse momentum sgd for pruning very deep neural networks"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "efficient facial feature learning with wide ensemble based convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "cnn based facial affect analysis on mobile devices"
    - "efficient diverse ensemble for discriminative co tracking"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "xception deep learning with depthwise separable convolutions"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (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"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-4 (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"
    - "label refinery improving imagenet classification through label progression"
    - "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-5 (induced by source paper "towards instance level image to image translation"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "rethinking imagenet pre training"
    - "cornernet detecting objects as paired keypoints"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "multimodal unsupervised image to image translation"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-6 (induced by source paper "stagewise knowledge distillation"):
  with members:
    - "faster gaze prediction with dense networks and fisher pruning"
    - "deep learning using rectified linear units relu"
    - "model compression via distillation and quantization"
    - "attention is all you need"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "hardware software codesign of accurate multiplier free deep neural networks"
    - "n2n learning network to network compression via policy gradient reinforcement learning"
    - "a survey of model compression and acceleration for deep 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)