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: "safer classification by synthesis"
  Abstract: The discriminative approach to classification using deep neural networks has become the de-facto standard in various fields. Complementing recent reservations about safety against adversarial examples, we show that conventional discriminative methods can easily be fooled to provide incorrect labels with very high confidence to out of distribution examples. We posit that a generative approach is the natural remedy for this problem, and propose a method for classification using generative models. At training time, we learn a generative model for each class, while at test time, given an example to classify, we query each generator for its most similar generation, and select the class corresponding to the most similar one. Our approach is general and can be used with expressive models such as GANs and VAEs. At test time, our method accurately "knows when it does not know," and provides resilience to out of distribution examples while maintaining competitive performance for standard examples.

Connected through hyperedge HE-1 (induced by source paper "deep residual flow for novelty detection"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "density estimation using real nvp"
    - "concrete problems in ai safety"
    - "universal adversarial perturbations"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "detecting out of distribution inputs in deep neural networks using an early layer output"):
  with members:
    - "learning deep features for one class classification"
    - "improving reconstruction autoencoder out of distribution detection with mahalanobis distance"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "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"
    - "deep bayesian active learning with image data"

Connected through hyperedge HE-3 (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"
    - "does your model know the digit 6 is not a cat a less biased evaluation of outlier detectors"
    - "certified defenses against adversarial examples"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"

Connected through hyperedge HE-4 (induced by source paper "training discriminative models to evaluate generative ones"):
  with members:
    - "pros and cons of gan evaluation measures"
    - "quantitatively evaluating gans with divergences proposed for training"
    - "began boundary equilibrium generative adversarial networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "rendergan generating realistic labeled data"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "out of distribution detection in classifiers via generation"):
  with members:
    - "analysis of confident classifiers for out of distribution detection"
    - "natural adversarial examples"
    - "deep anomaly detection with outlier exposure"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "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"
    - "on calibration of modern neural networks"
    - "emnist an extension of mnist to handwritten letters"

Connected through hyperedge HE-6 (induced by source paper "deep variational semi supervised novelty detection"):
  with members:
    - "deep semi supervised anomaly detection"
    - "deep learning for anomaly detection a survey"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "deep anomaly detection using geometric transformations"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "learning sampling distributions for robot motion planning"
    - "deep structured energy based models for anomaly detection"

Connected through hyperedge HE-7 (induced by source paper "analysis of confident classifiers for out of distribution detection"):
  with members:
    - "improving reconstruction autoencoder out of distribution detection with mahalanobis distance"
    - "a randomized gradient free attack on relu networks"
    - "deep anomaly detection with outlier exposure"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "denoising autoencoders for overgeneralization in neural networks"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-8 (induced by source paper "learning plannable representations with causal infogan"):
  with members:
    - "universal planning networks"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "temporal difference models model free deep rl for model based control"
    - "semi parametric topological memory for navigation"
    - "hindsight experience replay"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "visual interaction networks"
    - "progressive growing of gans for improved quality stability and variation"

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)