human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "training on the edge the why and the how"):
  with members:
    - "supporting very large models using automatic dataflow graph partitioning"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "high level python abstractions for optimal checkpointing in inversion problems"
    - "cutting down training memory by re fowarding"
    - "training deep nets with sublinear memory cost"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Question: Please tell me which class the center node 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)