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 "anomaly detection in video data based on probabilistic latent space models"):
  Abstract: This paper proposes a method for detecting anomalies in video data. A Variational Autoencoder (VAE) is used for reducing the dimensionality of video frames, generating latent space information that is comparable to low-dimensional sensory data (e.g., positioning, steering angle), making feasible the development of a consistent multi-modal architecture for autonomous vehicles. An Adapted Markov Jump Particle Filter defined by discrete and continuous inference levels is employed to predict the following frames and detecting anomalies in new video sequences. Our method is evaluated on different video scenarios where a semi-autonomous vehicle performs a set of tasks in a closed environment.

Includes members:
  Member "continual lifelong learning with neural networks a review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  Member "the limits and potentials of deep learning for robotics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda")
  Member "hierarchy of gans for learning embodied self awareness model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series")
  Member "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "monopair monocular 3d object detection using pairwise spatial relationships")
  Member "action recognition from static datasets to moving robots":
    also connected through other hyperedges:
      (none)
  Member "comparing deep neural networks against humans object recognition when the signal gets weaker":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rearchitecting classification frameworks for increased robustness")
  Member "a study and comparison of human and deep learning recognition performance under visual distortions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "training adversarial discriminators for cross channel abnormal event detection in crowds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series")

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)