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 "improved adversarial robustness by reducing open space risk via tent activations"):
  Abstract: Adversarial examples contain small perturbations that can remain imperceptible to human observers but alter the behavior of even the best performing deep learning models and yield incorrect outputs. Since their discovery, adversarial examples have drawn significant attention in machine learning: researchers try to reveal the reasons for their existence and improve the robustness of machine learning models to adversarial perturbations. The state-of-the-art defense is the computationally expensive and very time consuming adversarial training via projected gradient descent (PGD). We hypothesize that adversarial attacks exploit the open space risk of classic monotonic activation functions. This paper introduces the tent activation function with bounded open space risk and shows that tents make deep learning models more robust to adversarial attacks. We demonstrate on the MNIST dataset that a classifier with tents yields an average accuracy of 91.8% against six white-box adversarial attacks, which is more than 15 percentage points above the state of the art. On the CIFAR-10 dataset, our approach improves the average accuracy against the six white-box adversarial attacks to 73.5% from 41.8% achieved by adversarial training via PGD.

Includes members:
  Member "out of distribution detection using an ensemble of self supervised leave out classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "evidential deep learning to quantify classification uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  Member "efficient formal safety analysis of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "black box smoothing a provable defense for pretrained classifiers")
  Member "self normalizing neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "soft root sign activation function")
  Member "sparse representation based open set recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "conditional gaussian distribution learning for open set recognition")
  Member "extremeweather a large scale climate dataset for semi supervised detection localization and understanding of extreme weather events":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tackling climate change with machine learning")
  Member "noisy activation functions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "wide residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning representations by predicting bags of visual words")

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.CV(Computer Vision and Pattern Recognition)