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 "uncertainty aware anticipation of activities"):
  Abstract: Anticipating future activities in video is a task with many practical applications. While earlier approaches are limited to just a few seconds in the future, the prediction time horizon has just recently been extended to several minutes in the future. However, as increasing the predicted time horizon, the future becomes more uncertain and models that generate a single prediction fail at capturing the different possible future activities. In this paper, we address the uncertainty modelling for predicting long-term future activities. Both an action model and a length model are trained to model the probability distribution of the future activities. At test time, we sample from the predicted distributions multiple samples that correspond to the different possible sequences of future activities. Our model is evaluated on two challenging datasets and shows a good performance in capturing the multi-modal future activities without compromising the accuracy when predicting a single sequence of future activities.

Includes members:
  Member "action anticipation with rbf kernelized feature mapping rnn":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "action completion a temporal model for moment detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "temporal cycle consistency learning")
  Member "action anticipation by predicting future dynamic images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "diverse and admissible trajectory forecasting through multimodal context understanding")
  Member "when will you do what anticipating temporal occurrences of activities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "bayesian prediction of future street scenes using synthetic likelihoods":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "overcoming limitations of mixture density networks a sampling and fitting framework for multimodal future prediction")
  Member "weakly supervised action learning with rnn based fine to coarse modeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "encouraging lstms to anticipate actions very early":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ttpp temporal transformer with progressive prediction for efficient action anticipation")
  Member "visual forecasting by imitating dynamics in natural sequences":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "imitation learning for human pose prediction")

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)