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 "design light weight 3d convolutional networks for video recognition temporal residual fully separable block and fast algorithm"):
  Abstract: Deep 3-dimensional (3D) Convolutional Network (ConvNet) has shown promising performance on video recognition tasks because of its powerful spatio-temporal information fusion ability. However, the extremely intensive requirements on memory access and computing power prohibit it from being used in resource-constrained scenarios, such as portable and edge devices. So in this paper, we first propose a two-stage Fully Separable Block (FSB) to significantly compress the model sizes of 3D ConvNets. Then a feature enhancement approach named Temporal Residual Gradient (TRG) is developed to improve the performance of compressed model on video tasks, which provides higher accuracy, faster convergency and better robustness. Moreover, in order to further decrease the computing workload, we propose a hybrid Fast Algorithm (hFA) to drastically reduce the computation complexity of convolutions. These methods are effectively combined to design a light-weight and efficient ConvNet for video recognition tasks. Experiments on the popular dataset report 2.3x compression rate, 3.6x workload reduction, and 6.3% top-1 accuracy gain, over the state-of-the-art SlowFast model, which is already a highly compact model. The proposed methods also show good adaptability on traditional 3D ConvNet, demonstrating 7.4x more compact model, 11.0x less workload, and 3.0% higher accuracy

Includes members:
  Member "sgad soft guided adaptively dropped neural network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "batch shaping for learning conditional channel gated networks")
  Member "slowfast networks for video recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "motionnet joint perception and motion prediction for autonomous driving based on bird s eye view maps")
  Member "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lossless compression of deep neural networks")
  Member "convolutional two stream network fusion for video action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "spatiotemporal residual networks for video action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "long term temporal convolutions for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)