human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "detecting and diagnosing adversarial images with class conditional capsule reconstructions"
  Abstract: Adversarial examples raise questions about whether neural network models are sensitive to the same visual features as humans. Most of the proposed methods for mitigating adversarial examples have subsequently been defeated by stronger attacks. Motivated by these issues, we take a different approach and propose to instead detect adversarial examples based on class-conditional reconstructions of the input. Our method uses the reconstruction network proposed as part of Capsule Networks (CapsNets), but is general enough to be applied to standard convolutional networks. We find that adversarial or otherwise corrupted images result in much larger reconstruction errors than normal inputs, prompting a simple detection method by thresholding the reconstruction error. Based on these findings, we propose the Reconstructive Attack which seeks both to cause a misclassification and a low reconstruction error. While this attack produces undetected adversarial examples, we find that for CapsNets the resulting perturbations can cause the images to appear visually more like the target class. This suggests that CapsNets utilize features that are more aligned with human perception and address the central issue raised by adversarial examples.

Connected through hyperedge HE-1 (induced by source paper "controversial stimuli pitting neural networks against each other as models of human recognition"):
  with members:
    - "mnist c a robustness benchmark for computer vision"
    - "conditional generative models are not robust"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "humans can decipher adversarial images"
    - "deep predictive coding network for object recognition"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "towards the first adversarially robust neural network model on mnist"
    - "darccc detecting adversaries by reconstruction from class conditional capsules"

Connected through hyperedge HE-2 (induced by source paper "deflecting adversarial attacks"):
  with members:
    - "are odds really odd bypassing statistical detection of adversarial examples"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "on evaluating adversarial robustness"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "dynamic routing between capsules"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "increasing the adversarial robustness and explainability of capsule networks with gamma capsules"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "on the vulnerability of capsule networks to adversarial attacks"
    - "3d point capsule networks"
    - "videocapsulenet a simplified network for action detection"
    - "fast capsnet for lung cancer screening"
    - "dynamic routing between capsules"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Question: Please tell me which class the center node 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)