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 "concise and stable explanations using adversarial training"):
  Abstract: We show new connections between adversarial learning and explainability. One form of explanation of the output of a neural network model in terms of its input features, is a vector of feature-attributions using the Integrated Gradient (IG) method. Two desirable characteristics of an attribution-based explanation are: (1)$\mathit{sparseness}$: the attributions of irrelevant or weakly relevant features should be negligible, thus resulting in $\mathit{concise}$ explanations in terms of the significant features, and (2) $\mathit{stability}$: it should not vary significantly within a small local neighborhood of the input. For a general class of convex loss functions, we show two new theoretical results: (a) via a novel analysis of the Stochastic Gradient Descent process, we show that adversarial training using an $\ell_\infty$-bounded adversary produces sparse models (and hence concise explanations), and (b) we show that natural model-training while encouraging stable IG-explanations (via an extra term in the loss function), is equivalent to adversarial training. We demonstrate the first phenomenon in experiments training logistic regression models on a variety of tabular toy and real-world advertising datasets: adversarial training yields significantly sparser models, with little or no degradation in performance on natural test data.

Includes members:
  Member "adversarial deep learning for robust detection of binary encoded malware":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "effectiveness of adversarial examples and defenses for malware classification")
  Member "explanation methods in deep learning users values concerns and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "questioning the ai informing design practices for explainable ai user experiences")
  Member "a new angle on l2 regularization":
    also connected through other hyperedges:
      (none)
  Member "adversarial logit pairing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast is better than free revisiting adversarial training")
  Member "adversarially robust generalization requires more data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "a survey of methods for explaining black box models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "vice visual counterfactual explanations for machine learning models")
  Member "influence directed explanations for deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stakeholders in explainable ai")
  Member "regularizing deep networks using efficient layerwise adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")

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)