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 "uninformed students student teacher anomaly detection with discriminative latent embeddings"):
  Abstract: We introduce a simple, yet powerful student-teacher framework for the challenging problem of unsupervised anomaly detection and pixel-precise anomaly segmentation in high-resolution images. To circumvent the need for prior data labeling, student networks are trained to regress the output of a descriptive teacher network that was pretrained on a large dataset of patches from natural images. Anomalies are detected when the student networks fail to generalize outside the manifold of anomaly-free training data, i.e., when the output of the student networks differ from that of the teacher network. Additionally, the intrinsic uncertainty in the student networks can be used as a scoring function that indicates anomalies. We compare our method to a large number of existing deep-learning-based methods for unsupervised anomaly detection. Our experiments demonstrate improvements over state-of-the-art methods on a number of real-world datasets, including the recently introduced MVTec Anomaly Detection dataset that was specifically designed to benchmark anomaly segmentation algorithms.

Includes members:
  Member "improving unsupervised defect segmentation by applying structural similarity to autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "iterative energy based projection on a normal data manifold for anomaly localization")
  Member "deep transfer learning for multiple class novelty detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi class novelty detection using mix up technique")
  Member "ocgan one class novelty detection using gans with constrained latent representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")
  Member "do better imagenet models transfer better":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "informed democracy voting based novelty detection for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications")
  Member "are pre trained cnns good feature extractors for anomaly detection in surveillance videos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "3d resnet with ranking loss function for abnormal activity detection in videos")
  Member "latent space autoregression for novelty detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")
  Member "deep autoencoding models for unsupervised anomaly segmentation in brain mr images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "synthesize then compare detecting failures and anomalies for semantic segmentation")

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.CV(Computer Vision and Pattern Recognition)