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: "out of distribution detection using an ensemble of self supervised leave out classifiers"
  Abstract: As deep learning methods form a critical part in commercially important applications such as autonomous driving and medical diagnostics, it is important to reliably detect out-of-distribution (OOD) inputs while employing these algorithms. In this work, we propose an OOD detection algorithm which comprises of an ensemble of classifiers. We train each classifier in a self-supervised manner by leaving out a random subset of training data as OOD data and the rest as in-distribution (ID) data. We propose a novel margin-based loss over the softmax output which seeks to maintain at least a margin $m$ between the average entropy of the OOD and in-distribution samples. In conjunction with the standard cross-entropy loss, we minimize the novel loss to train an ensemble of classifiers. We also propose a novel method to combine the outputs of the ensemble of classifiers to obtain OOD detection score and class prediction. Overall, our method convincingly outperforms Hendrycks et al.[7] and the current state-of-the-art ODIN[13] on several OOD detection benchmarks.

Connected through hyperedge HE-1 (induced by source paper "anomalous instance detection in deep learning a survey"):
  with members:
    - "unmask adversarial detection and defense through robust feature alignment"
    - "a research agenda dynamic models to defend against correlated attacks"
    - "analysis of confident classifiers for out of distribution detection"
    - "adversarial attacks on time series"
    - "adversarial attacks on deep neural networks for time series classification"
    - "mimicgan robust projection onto image manifolds with corruption mimicking"
    - "analyzing adversarial attacks against deep learning for intrusion detection in iot networks"
    - "deep learning for anomaly detection a survey"

Connected through hyperedge HE-2 (induced by source paper "multi class novelty detection using mix up technique"):
  with members:
    - "deep transfer learning for multiple class novelty detection"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "adversarially learned one class classifier for novelty detection"
    - "latent space autoregression for novelty detection"
    - "hierarchical novelty detection for visual object recognition"
    - "metric learning for novelty and anomaly detection"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-3 (induced by source paper "generalized odin detecting out of distribution image without learning from out of distribution data"):
  with members:
    - "hyperparameter free out of distribution detection using softmax of scaled cosine similarity"
    - "moment matching for multi source domain adaptation"
    - "reducing network agnostophobia"
    - "deep anomaly detection with outlier exposure"
    - "metric learning for novelty and anomaly detection"
    - "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-4 (induced by source paper "dropout strikes back improved uncertainty estimation via diversity sampled implicit ensembles"):
  with members:
    - "maximizing overall diversity for improved uncertainty estimates in deep ensembles"
    - "subspace inference for bayesian deep learning"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "dropout based active learning for regression"
    - "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"
    - "deep bayesian active learning with image data"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-5 (induced by source paper "out of distribution detection in multi label datasets using latent space of beta vae"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "improving reconstruction autoencoder out of distribution detection with mahalanobis distance"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-6 (induced by source paper "practical solutions for machine learning safety in autonomous vehicles"):
  with members:
    - "predicting model failure using saliency maps in autonomous driving systems"
    - "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"
    - "selectivenet a deep neural network with an integrated reject option"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "using pre training can improve model robustness and uncertainty"
    - "evaluating uncertainty quantification in end to end autonomous driving control"
    - "training verified learners with learned verifiers"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"

Connected through hyperedge HE-7 (induced by source paper "simultaneous semantic segmentation and outlier detection in presence of domain shift"):
  with members:
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "efficient ladder style densenets for semantic segmentation of large images"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "taskonomy disentangling task transfer learning"
    - "pixel wise attentional gating for parsimonious pixel labeling"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection with outlier exposure"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-8 (induced by source paper "unsupervised out of distribution detection with batch normalization"):
  with members:
    - "neural processes"
    - "are generative deep models for novelty detection truly better"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "group anomaly detection using deep generative models"
    - "deep anomaly detection with outlier exposure"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "novelty detection with gan"

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)