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 "optimal gradient checkpoint search for arbitrary computation graphs"):
  Abstract: Deep Neural Networks(DNNs) require huge GPU memory when training on modern image/video databases. Unfortunately, the GPU memory in off-the-shelf devices is always finite, which limits the image resolutions and batch sizes that could be used for better DNN performance. Existing approaches to alleviate memory issue include better GPUs, distributed computation and gradient checkpointing. Among them, gradient checkpointing is a favorable approach as it focuses on trading computation for memory and does not require any upgrades on hardware. In gradient checkpointing, during forward, only a subset of intermediate tensors are stored, which are called Gradient Checkpoints (GCPs). Then during backward, extra local forwards are conducted to compute the missing tensors. The total training memory cost becomes the sum of (1) the memory cost of the gradient checkpoints and (2) the maximum memory cost of local forwards. To achieve maximal memory cut-offs, one needs optimal algorithms to select GCPs. #R##N#Existing gradient checkpointing approaches rely on either manual input of GCPs or heuristics-based GCP search on linear computation graphs (LCGs), and cannot apply to arbitrary computation graphs(ACGs). In this paper, we present theories and optimal algorithms on GCP selection that, for the first time, apply to ACGs and achieve maximal memory cut-offs. Extensive experiments show that our approach constantly outperforms existing approaches on LCGs, and can cut off up-to 80% of training memory with a moderate time overhead (around 40%) on LCG and ACG DNNs, such as Alexnet, VGG, Resnet, Densenet and Inception Net.

Includes members:
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "superneurons dynamic gpu memory management for training deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding and optimizing packed neural network training for hyper parameter tuning")
  Member "learning spatio temporal features with 3d residual networks for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "r fcn 3000 at 30fps decoupling detection and classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless compression of deep neural networks")
  Member "vdnn virtualized deep neural networks for scalable memory efficient neural network design":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an empirical evaluation of perturbation based defenses")
  Member "training deep nets with sublinear memory cost":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "searching for winograd aware quantized networks")

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.LG(Machine Learning)