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 "context encoding variational autoencoder for unsupervised anomaly detection"):
  Abstract: Unsupervised learning can leverage large-scale data sources without the need for annotations. In this context, deep learning-based auto encoders have shown great potential in detecting anomalies in medical images. However, state-of-the-art anomaly scores are still based on the reconstruction error, which lacks in two essential parts: it ignores the model-internal representation employed for reconstruction, and it lacks formal assertions and comparability between samples. We address these shortcomings by proposing the Context-encoding Variational Autoencoder (ceVAE) which combines reconstruction- with density-based anomaly scoring. This improves the sample- as well as pixel-wise results. In our experiments on the BraTS-2017 and ISLES-2015 segmentation benchmarks, the ceVAE achieves unsupervised ROC-AUCs of 0.95 and 0.89, respectively, thus outperforming state-of-the-art methods by a considerable margin.

Includes members:
  Member "unsupervised detection of lesions in brain mri using constrained adversarial auto encoders":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "boosting segmentation with weak supervision from image to image translation")
  Member "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "strategy to increase the safety of a dnn based perception for had systems")
  Member "deep generative models in the real world an open challenge from medical imaging":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised anomaly localization using variational auto encoders")
  Member "an intriguing failing of convolutional neural networks and the coordconv solution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "deep autoencoding models for unsupervised anomaly segmentation in brain mr images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "synthesize then compare detecting failures and anomalies for semantic segmentation")
  Member "sanity checks for saliency maps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "smoothgrad removing noise by adding noise":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "context encoders feature learning by inpainting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning representations by predicting bags of visual words")

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)