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 "adversarially trained model compression when robustness meets efficiency"):
  Abstract: The robustness of deep models to adversarial attacks has gained significant attention in recent years, so has the model compactness and efficiency: yet the two have been mostly studied separately, with few relationships drawn between each other. This paper is concerned with: how can we combine the best of both worlds, obtaining a robust and compact network? The answer is not as straightforward as it may seem, since the two goals of model robustness and compactness may contradict from time to time. We formally study this new question, by proposing a novel Adversarially Trained Model Compression (ATMC) framework. A unified constrained optimization formulation is designed, with an efficient algorithm developed. An extensive group of experiments are then carefully designed and presented, demonstrating that ATMC obtains remarkably more favorable trade-off among model size, accuracy and robustness, over currently available alternatives in various settings.

Includes members:
  Member "learning sparsity and quantization jointly and automatically for neural network compression via constrained optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "model compression with adversarial robustness a unified optimization framework")
  Member "adversarial robustness may be at odds with simplicity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "triple wins boosting accuracy robustness and efficiency together by enabling input adaptive inference")
  Member "defensive quantization when efficiency meets robustness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "triple wins boosting accuracy robustness and efficiency together by enabling input adaptive inference")
  Member "dual dynamic inference enabling more efficient adaptive and controllable deep inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "triple wins boosting accuracy robustness and efficiency together by enabling input adaptive inference")
  Member "towards imperceptible and robust adversarial example attacks against neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "sparse dnns with improved adversarial robustness":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on pruning adversarially robust neural networks")
  Member "stochastic activation pruning for robust adversarial defense":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "post training piecewise linear quantization for deep neural networks")

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)