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 "rearchitecting classification frameworks for increased robustness"):
  Abstract: While generalizing well over natural inputs, neural networks are vulnerable to adversarial inputs. Existing defenses against adversarial inputs have largely been detached from the real world. These defenses also come at a cost to accuracy. Fortunately, there are invariances of an object that are its salient features; when we break them it will necessarily change the perception of the object. We find that applying invariants to the classification task makes robustness and accuracy feasible together. Two questions follow: how to extract and model these invariances? and how to design a classification paradigm that leverages these invariances to improve the robustness accuracy trade-off? The remainder of the paper discusses solutions to the aformenetioned questions.

Includes members:
  Member "adversarial sensor attack on lidar based perception in autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ghostimage perception domain attacks against vision based object classification systems")
  Member "extending adversarial attacks and defenses to deep 3d point cloud classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "samplenet differentiable point cloud sampling")
  Member "certified adversarial robustness via randomized smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "a stratified approach to robustness for randomly smoothed classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial data encryption")
  Member "humans can decipher adversarial images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a hierarchy of limitations in machine learning")
  Member "exploiting excessive invariance caused by norm bounded adversarial robustness":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on adaptive attacks to adversarial example defenses")
  Member "on evaluating adversarial robustness":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "anomalous instance detection in deep learning a survey")
  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)