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: "confidence estimation in deep neural networks via density modelling"
  Abstract: State-of-the-art Deep Neural Networks can be easily fooled into providing incorrect high-confidence predictions for images with small amounts of adversarial noise. Does this expose a flaw with deep neural networks, or do we simply need a better way to estimate confidence? In this paper we consider the problem of accurately estimating predictive confidence. We formulate this problem as that of density modelling, and show how traditional methods such as softmax produce poor estimates. To address this issue, we propose a novel confidence measure based on density modelling approaches. We test these measures on images distorted by blur, JPEG compression, random noise and adversarial noise. Experiments show that our confidence measure consistently shows reduced confidence scores in the presence of such distortions - a property which softmax often lacks.

Connected through hyperedge HE-1 (induced by source paper "hyperparameter free out of distribution detection using softmax of scaled cosine similarity"):
  with members:
    - "using pre training can improve model robustness and uncertainty"
    - "lightweight probabilistic deep networks"
    - "leveraging uncertainty estimates for predicting segmentation quality"
    - "confidence scoring using whitebox meta models with linear classifier probes"
    - "arcface additive angular margin loss for deep face recognition"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "deep anomaly detection with outlier exposure"
    - "accurate uncertainties for deep learning using calibrated regression"

Connected through hyperedge HE-2 (induced by source paper "accurate layerwise interpretable competence estimation"):
  with members:
    - "evaluation of neural network uncertainty estimation with application to resource constrained platforms"
    - "classification uncertainty of deep neural networks based on gradient information"
    - "confidence scoring using whitebox meta models with linear classifier probes"
    - "yolov3 an incremental improvement"
    - "on calibration of modern neural networks"
    - "prototypical networks for few shot learning"
    - "distance based confidence score for neural network classifiers"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-3 (induced by source paper "deep confidence a computationally efficient framework for calculating reliable prediction errors for deep neural networks"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "on calibration of modern neural networks"
    - "molecular de novo design through deep reinforcement learning"
    - "exploring loss function topology with cyclical learning rates"
    - "low data drug discovery with one shot learning"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "towards open set deep networks"
    - "cyclical learning rates for training neural networks"

Connected through hyperedge HE-4 (induced by source paper "leveraging simple model predictions for enhancing its performance"):
  with members:
    - "energy and policy considerations for deep learning in nlp"
    - "improving simple models with confidence profiles"
    - "methods for interpreting and understanding deep neural networks"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "learning to reweight examples for robust deep learning"
    - "on calibration of modern neural networks"
    - "tip typifying the interpretability of procedures"
    - "interpreting blackbox models via model extraction"

Connected through hyperedge HE-5 (induced by source paper "deep anomaly detection with outlier exposure"):
  with members:
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "an analysis of neural language modeling at multiple scales"
    - "exploring the limits of weakly supervised pretraining"
    - "discriminative out of distribution detection for semantic segmentation"
    - "learning to generate reviews and discovering sentiment"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-6 (induced by source paper "progressive neural networks for image classification"):
  with members:
    - "on calibration of modern neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "noscope optimizing neural network queries over video at scale"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "neural architecture search with reinforcement learning"

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)