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 "future frame prediction for anomaly detection a new baseline"):
  Abstract: Anomaly detection in videos refers to the identification of events that do not conform to expected behavior. However, almost all existing methods tackle the problem by minimizing the reconstruction errors of training data, which cannot guarantee a larger reconstruction error for an abnormal event. In this paper, we propose to tackle the anomaly detection problem within a video prediction framework. To the best of our knowledge, this is the first work that leverages the difference between a predicted future frame and its ground truth to detect an abnormal event. To predict a future frame with higher quality for normal events, other than the commonly used appearance (spatial) constraints on intensity and gradient, we also introduce a motion (temporal) constraint in video prediction by enforcing the optical flow between predicted frames and ground truth frames to be consistent, and this is the first work that introduces a temporal constraint into the video prediction task. Such spatial and motion constraints facilitate the future frame prediction for normal events, and consequently facilitate to identify those abnormal events that do not conform the expectation. Extensive experiments on both a toy dataset and some publicly available datasets validate the effectiveness of our method in terms of robustness to the uncertainty in normal events and the sensitivity to abnormal events

Includes members:
  Member "decomposing motion and content for natural video sequence prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "abnormal event detection in videos using generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning a distance function with a siamese network to localize anomalies in videos")
  Member "unmasking the abnormal events in video":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self trained deep ordinal regression for end to end video anomaly detection")
  Member "joint detection and recounting of abnormal events by learning deep generic knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self trained deep ordinal regression for end to end video anomaly detection")
  Member "abnormal event detection in videos using spatiotemporal autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self trained deep ordinal regression for end to end video anomaly detection")
  Member "video imagination from a single image with transformation generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning predictive models from observation and interaction")
  Member "transformation based models of video sequences":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "first order motion model for image animation")
  Member "a discriminative framework for anomaly detection in large videos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "self trained deep ordinal regression for end to end video anomaly detection")

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)