human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "supermix supervising the mixing data augmentation"):
  Abstract: In this paper, we propose a supervised mixing augmentation method, termed SuperMix, which exploits the knowledge of a teacher to mix images based on their salient regions. SuperMix optimizes a mixing objective that considers: i) forcing the class of input images to appear in the mixed image, ii) preserving the local structure of images, and iii) reducing the risk of suppressing important features. To make the mixing suitable for large-scale applications, we develop an optimization technique, $65\times$ faster than gradient descent on the same problem. We validate the effectiveness of SuperMix through extensive evaluations and ablation studies on two tasks of object classification and knowledge distillation. On the classification task, SuperMix provides the same performance as the advanced augmentation methods, such as AutoAugment. On the distillation task, SuperMix sets a new state-of-the-art with a significantly simplified distillation method. Particularly, in six out of eight teacher-student setups from the same architectures, the students trained on the mixed data surpass their teachers with a notable margin.

Includes members:
  Member "cutmix regularization strategy to train strong classifiers with localizable features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words")
  Member "population based augmentation efficient learning of augmentation policy schedules":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "relational knowledge distillation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "randaugment practical automated data augmentation with a reduced search space":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "randaugment practical data augmentation with no separate search":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on feature normalization and data augmentation")
  Member "variational information distillation for knowledge transfer":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "freeze discriminator a simple baseline for fine tuning gans")
  Member "fast autoaugment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on feature normalization and data augmentation")
  Member "contrastive representation distillation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "knowledge distillation via adaptive instance normalization")

Question: Please tell me which class the source paper 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)