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: "comparing deep neural networks against humans object recognition when the signal gets weaker"
  Abstract: Human visual object recognition is typically rapid and seemingly effortless, as well as largely independent of viewpoint and object orientation. Until very recently, animate visual systems were the only ones capable of this remarkable computational feat. This has changed with the rise of a class of computer vision algorithms called deep neural networks (DNNs) that achieve human-level classification performance on object recognition tasks. Furthermore, a growing number of studies report similarities in the way DNNs and the human visual system process objects, suggesting that current DNNs may be good models of human visual object recognition. Yet there clearly exist important architectural and processing differences between state-of-the-art DNNs and the primate visual system. The potential behavioural consequences of these differences are not well understood. We aim to address this issue by comparing human and DNN generalisation abilities towards image degradations. We find the human visual system to be more robust to image manipulations like contrast reduction, additive noise or novel eidolon-distortions. In addition, we find progressively diverging classification error-patterns between humans and DNNs when the signal gets weaker, indicating that there may still be marked differences in the way humans and current DNNs perform visual object recognition. We envision that our findings as well as our carefully measured and freely available behavioural datasets provide a new useful benchmark for the computer vision community to improve the robustness of DNNs and a motivation for neuroscientists to search for mechanisms in the brain that could facilitate this robustness.

Connected through hyperedge HE-1 (induced by source paper "anomaly detection in video data based on probabilistic latent space models"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "the limits and potentials of deep learning for robotics"
    - "hierarchy of gans for learning embodied self awareness model"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "action recognition from static datasets to moving robots"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"
    - "training adversarial discriminators for cross channel abnormal event detection in crowds"

Connected through hyperedge HE-2 (induced by source paper "rearchitecting classification frameworks for increased robustness"):
  with members:
    - "adversarial sensor attack on lidar based perception in autonomous driving"
    - "extending adversarial attacks and defenses to deep 3d point cloud classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "a stratified approach to robustness for randomly smoothed classifiers"
    - "humans can decipher adversarial images"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "on evaluating adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"

Connected through hyperedge HE-3 (induced by source paper "defective convolutional layers learn robust cnns"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "dropping pixels for adversarial robustness"
    - "adversarial defense by stratified convolutional sparse coding"
    - "dropblock a regularization method for convolutional networks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "analyzing the noise robustness of deep neural networks"
    - "sok security and privacy in machine learning"

Connected through hyperedge HE-4 (induced by source paper "orthogonal deep neural networks"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the singular values of convolutional layers"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "on the effect of pooling on the geometry of representations"
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"
    - "data dependent stability of stochastic gradient descent"
    - "sharp minima can generalize for deep nets"
    - "global optimality conditions for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "manifestation of image contrast in deep networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "how is contrast encoded in deep neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"

Connected through hyperedge HE-6 (induced by source paper "training with the invisibles obfuscating images to share safely for learning visual recognition models"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to anonymize faces for privacy preserving action detection"
    - "a survey on data collection for machine learning a big data ai integration perspective"
    - "on classification of distorted images with deep convolutional neural networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "adversarial image perturbation for privacy protection a game theory perspective"
    - "toward multimodal image to image translation"

Connected through hyperedge HE-7 (induced by source paper "benchmarking neural network robustness to common corruptions and perturbations"):
  with members:
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "adversarial spheres"
    - "cure or challenging unreal and real environments for object recognition"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"

Connected through hyperedge HE-8 (induced by source paper "assessment of faster r cnn in man machine collaborative search"):
  with members:
    - "adversarial attacks against medical deep learning systems"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "attention allocation aid for visual search"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "improving small object proposals for company logo detection"
    - "machine teaching a new paradigm for building machine learning systems"
    - "a survey on deep learning in medical image analysis"

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.CV(Computer Vision and Pattern Recognition)