human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "out of distribution detection with distance guarantee in deep generative models"):
  Abstract: Recent research has shown that it is challenging to detect out-of-distribution (OOD) data in deep generative models including flow-based models and variational autoencoders (VAEs). In this paper, we prove a theorem that, for a well-trained flow-based model, the distance between the distribution of representations of an OOD dataset and prior can be large enough, as long as the distance between the distributions of the training dataset and the OOD dataset is large enough. Furthermore, our observation shows that, for flow-based model and VAE with factorized prior, the representations of OOD datasets are more correlated than that of the training dataset. Based on our theorem and observation, we propose detecting OOD data according to the total correlation of representations in flow-based model and VAE. Experimental results show that our method can achieve nearly 100\% AUROC for all the widely used benchmarks and has robustness against data manipulation. While the state-of-the-art method performs not better than random guessing for challenging problems and can be fooled by data manipulation in almost all cases.

Includes members:
  Member "conditional generative models are not robust":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exact information bottleneck with invertible neural networks getting the best of discriminative and generative modeling")
  Member "are generative deep models for novelty detection truly better":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "advae a self adversarial variational autoencoder with gaussian anomaly prior knowledge for anomaly detection")
  Member "challenging common assumptions in the unsupervised learning of disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "group anomaly detection using deep generative models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised out of distribution detection with batch normalization")
  Member "deep anomaly detection with outlier exposure":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "isolating sources of disentanglement in variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "low resource knowledge grounded dialogue generation")
  Member "large scale gan training for high fidelity natural image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "does your model know the digit 6 is not a cat a less biased evaluation of outlier detectors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "anomalous instance detection in deep learning a survey")

Question: Please tell me which class the source paper 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)