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: "snapshot distillation teacher student optimization in one generation"
  Abstract: Optimizing a deep neural network is a fundamental task in computer vision, yet direct training methods often suffer from over-fitting. Teacher-student optimization aims at providing complementary cues from a model trained previously, but these approaches are often considerably slow due to the pipeline of training a few generations in sequence, i.e., time complexity is increased by several times. #R##N#This paper presents snapshot distillation (SD), the first framework which enables teacher-student optimization in one generation. The idea of SD is very simple: instead of borrowing supervision signals from previous generations, we extract such information from earlier epochs in the same generation, meanwhile make sure that the difference between teacher and student is sufficiently large so as to prevent under-fitting. To achieve this goal, we implement SD in a cyclic learning rate policy, in which the last snapshot of each cycle is used as the teacher for all iterations in the next cycle, and the teacher signal is smoothed to provide richer information. In standard image classification benchmarks such as CIFAR100 and ILSVRC2012, SD achieves consistent accuracy gain without heavy computational overheads. We also verify that models pre-trained with SD transfers well to object detection and semantic segmentation in the PascalVOC dataset.

Connected through hyperedge HE-1 (induced by source paper "long short term sample distillation"):
  with members:
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on calibration of modern neural networks"
    - "snapshot ensembles train 1 get m for free"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "sobolev training for neural networks"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "temporal ensembling for semi supervised learning"

Connected through hyperedge HE-2 (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"
    - "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"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-3 (induced by source paper "dmcl distillation multiple choice learning for multimodal action recognition"):
  with members:
    - "unifying heterogeneous classifiers with distillation"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "learning with privileged information via adversarial discriminative modality distillation"
    - "human action recognition and prediction a survey"
    - "action recognition for depth video using multi view dynamic images"
    - "motion feature network fixed motion filter for action recognition"
    - "modality distillation with multiple stream networks for action recognition"
    - "representation flow for action recognition"

Connected through hyperedge HE-4 (induced by source paper "a generalized and robust method towards practical gaze estimation on smart phone"):
  with members:
    - "evaluation of appearance based methods and implications for gaze based applications"
    - "few shot adaptive gaze estimation"
    - "improving few shot user specific gaze adaptation via gaze redirection synthesis"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "feature denoising for improving adversarial robustness"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "repr improved training of convolutional filters"

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)