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 "learning from brains how to regularize machines"):
  Abstract: Despite impressive performance on numerous visual tasks, Convolutional Neural Networks (CNNs) --- unlike brains --- are often highly sensitive to small perturbations of their input, e.g. adversarial noise leading to erroneous decisions. We propose to regularize CNNs using large-scale neuroscience data to learn more robust neural features in terms of representational similarity. We presented natural images to mice and measured the responses of thousands of neurons from cortical visual areas. Next, we denoised the notoriously variable neural activity using strong predictive models trained on this large corpus of responses from the mouse visual system, and calculated the representational similarity for millions of pairs of images from the model's predictions. We then used the neural representation similarity to regularize CNNs trained on image classification by penalizing intermediate representations that deviated from neural ones. This preserved performance of baseline models when classifying images under standard benchmarks, while maintaining substantially higher performance compared to baseline or control models when classifying noisy images. Moreover, the models regularized with cortical representations also improved model robustness in terms of adversarial attacks. This demonstrates that regularizing with neural data can be an effective tool to create an inductive bias towards more robust inference.

Includes members:
  Member "similarity of neural network representations revisited":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explaining knowledge distillation by quantifying the knowledge")
  Member "approximating cnns with bag of local features models works surprisingly well on imagenet":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning representations by predicting bags of visual words")
  Member "adversarial risk and the dangers of evaluating against weak attacks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "black box smoothing a provable defense for pretrained classifiers")
  Member "a neurobiological evaluation metric for neural network model search":
    also connected through other hyperedges:
      (none)
  Member "towards the first adversarially robust neural network model on mnist":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise")
  Member "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses":
    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 "generalisation in humans and deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an empirical evaluation on robustness and uncertainty of regularization methods")
  Member "sparsefool a few pixels make a big difference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")

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)