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: "does your model know the digit 6 is not a cat a less biased evaluation of outlier detectors"
  Abstract: In the real world, a learning system could receive an input that looks nothing like anything it has seen during training, and this can lead to unpredictable behaviour. We thus need to know whether any given input belongs to the population distribution of the training data to prevent unpredictable behaviour in deployed systems. A recent surge of interest on this problem has led to the development of sophisticated techniques in the deep learning literature. However, due to the absence of a standardized problem formulation or an exhaustive evaluation, it is not evident if we can rely on these methods in practice. What makes this problem different from a typical supervised learning setting is that we cannot model the diversity of out-of-distribution samples in practice. The distribution of outliers used in training may not be the same as the distribution of outliers encountered in the application. Therefore, classical approaches that learn inliers vs. outliers with only two datasets can yield optimistic results. We introduce OD-test, a three-dataset evaluation scheme as a practical and more reliable strategy to assess progress on this problem. The OD-test benchmark provides a straightforward means of comparison for methods that address the out-of-distribution sample detection problem. We present an exhaustive evaluation of a broad set of methods from related areas on image classification tasks. Furthermore, we show that for realistic applications of high-dimensional images, the existing methods have low accuracy. Our analysis reveals areas of strength and weakness of each method.

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 "out of distribution detection with distance guarantee in deep generative models"):
  with members:
    - "conditional generative models are not robust"
    - "are generative deep models for novelty detection truly better"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "group anomaly detection using deep generative models"
    - "deep anomaly detection with outlier exposure"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "variational inference of disentangled latent concepts from unlabeled observations"

Connected through hyperedge HE-3 (induced by source paper "granular learning with deep generative models using highly contaminated data"):
  with members:
    - "deep generative models strike back improving understanding and evaluation in light of unmet expectations for ood data"
    - "revisiting self supervised visual representation learning"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "deep anomaly detection with outlier exposure"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "density estimation using real nvp"
    - "identity mappings in deep residual networks"
    - "made masked autoencoder for distribution estimation"

Connected through hyperedge HE-4 (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"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection with outlier exposure"

Connected through hyperedge HE-5 (induced by source paper "out of domain detection for natural language understanding in dialog systems"):
  with members:
    - "open world learning and application to product classification"
    - "efficient large scale domain classification with personalized attention"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "learning from positive and unlabeled data a survey"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"
    - "generative probabilistic novelty detection with adversarial autoencoders"

Connected through hyperedge HE-6 (induced by source paper "bayesian variational autoencoders for unsupervised out of distribution detection"):
  with members:
    - "a cold approach to generating optimal samples"
    - "conditioning by adaptive sampling for robust design"
    - "all smiles variational autoencoder"
    - "detecting semantic anomalies"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "icebreaker element wise active information acquisition with bayesian deep latent gaussian model"
    - "efficient gan based anomaly detection"
    - "uncertainty in the variational information bottleneck"

Connected through hyperedge HE-7 (induced by source paper "interpretable image recognition with hierarchical prototypes"):
  with members:
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "this looks like that deep learning for interpretable image recognition"
    - "branchconnect image categorization with learned branch connections"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "network dissection quantifying interpretability of deep visual representations"
    - "axiomatic attribution for deep networks"
    - "b cnn branch convolutional neural network for hierarchical classification"

Connected through hyperedge HE-8 (induced by source paper "towards neural networks that provably know when they don t know"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "better the devil you know an analysis of evasion attacks using out of distribution adversarial examples"
    - "towards the first adversarially robust neural network model on mnist"
    - "deep anomaly detection with outlier exposure"
    - "evidential deep learning to quantify classification uncertainty"
    - "certified defenses against adversarial examples"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "enhancing the reliability of out of distribution image detection in 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)