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: "towards neural networks that provably know when they don t know"
  Abstract: It has recently been shown that ReLU networks produce arbitrarily over-confident predictions far away from the training data. Thus, ReLU networks do not know when they don't know. However, this is a highly important property in safety critical applications. In the context of out-of-distribution detection (OOD) there have been a number of proposals to mitigate this problem but none of them are able to make any mathematical guarantees. In this paper we propose a new approach to OOD which overcomes both problems. Our approach can be used with ReLU networks and provides provably low confidence predictions far away from the training data as well as the first certificates for low confidence predictions in a neighborhood of an out-distribution point. In the experiments we show that state-of-the-art methods fail in this worst-case setting whereas our model can guarantee its performance while retaining state-of-the-art OOD performance.

Connected through hyperedge HE-1 (induced by source paper "no surprises training robust lung nodule detection for low dose ct scans by augmenting with adversarial attacks"):
  with members:
    - "understanding adversarial attacks on deep learning based medical image analysis systems"
    - "correlation via synthesis end to end nodule image generation and radiogenomic map learning based on generative adversarial network"
    - "data augmentation with manifold exploring geometric transformations for increased performance and robustness"
    - "generative adversarial network in medical imaging a review"
    - "batch normalization is a cause of adversarial vulnerability"
    - "synthesizing diverse lung nodules wherever massively 3d multi conditional gan based ct image augmentation for object detection"
    - "quantifying and leveraging classification uncertainty for chest radiograph assessment"
    - "class aware adversarial lung nodule synthesis in ct images"

Connected through hyperedge HE-2 (induced by source paper "natural adversarial examples"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "verified uncertainty calibration"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "detecting semantic anomalies"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "on evaluating adversarial robustness"

Connected through hyperedge HE-3 (induced by source paper "detection of false positive and false negative samples in semantic segmentation"):
  with members:
    - "uncertainty measures and prediction quality rating for the semantic segmentation of nested multi resolution street scene images"
    - "time dynamic estimates of the reliability of deep semantic segmentation networks"
    - "aleatoric and epistemic uncertainty in machine learning a tutorial introduction"
    - "the ethical dilemma when not setting up cost based decision rules in semantic segmentation"
    - "application of decision rules for handling class imbalance in semantic segmentation"
    - "leveraging uncertainty estimates for predicting segmentation quality"
    - "classification uncertainty of deep neural networks based on gradient information"
    - "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities"

Connected through hyperedge HE-4 (induced by source paper "on manifold adversarial data augmentation improves uncertainty calibration"):
  with members:
    - "large scale adversarial representation learning"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "measuring calibration in deep learning"
    - "human uncertainty makes classification more robust"
    - "disentangling adversarial robustness and generalization"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "on calibration of modern neural networks"
    - "selective classification for deep neural networks"

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)