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 "affinity and diversity quantifying mechanisms of data augmentation"):
  Abstract: Though data augmentation has become a standard component of deep neural network training, the underlying mechanism behind the effectiveness of these techniques remains poorly understood. In practice, augmentation policies are often chosen using heuristics of either distribution shift or augmentation diversity. Inspired by these, we seek to quantify how data augmentation improves model generalization. To this end, we introduce interpretable and easy-to-compute measures: Affinity and Diversity. We find that augmentation performance is predicted not by either of these alone but by jointly optimizing the two.

Includes members:
  Member "fixmatch simplifying semi supervised learning with consistency and confidence":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "domain adaptive ensemble learning")
  Member "faster autoaugment learning augmentation strategies using backpropagation":
    also connected through other hyperedges:
      (none)
  Member "starnet targeted computation for object detection in point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nuscenes a multimodal dataset for autonomous driving")
  Member "improving robustness without sacrificing accuracy with patch gaussian augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise")
  Member "population based augmentation efficient learning of augmentation policy schedules":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "augmented parallel pyramid net for attention guided pose estimation")
  Member "adversarial examples are a natural consequence of test error in noise":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise")
  Member "your classifier is secretly an energy based model and you should treat it like one":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reliable evaluation of adversarial robustness with an ensemble of diverse parameter free attacks")
  Member "efficientdet scalable and efficient object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "slimconv reducing channel redundancy in convolutional neural networks by weights flipping")

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.LG(Machine Learning)