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 "understanding and optimizing packed neural network training for hyper parameter tuning"):
  Abstract: As neural networks are increasingly employed in machine learning practice, organizations will have to determine how to share limited training resources among a diverse set of model training tasks. This paper studies jointly training multiple neural network models on a single GPU. We presents an empirical study of this operation, called pack, and end-to-end experiments that suggest significant improvements for hyperparameter search systems. Our research prototype is in TensorFlow, and we evaluate performance across different models (ResNet, MobileNet, DenseNet, and MLP) and training scenarios. The results suggest: (1) packing two models can bring up to 40% performance improvement over unpacked setups for a single training step and the improvement increases when packing more models; (2) the benefit of a pack primitive largely depends on a number of factors including memory capacity, chip architecture, neural network structure, and batch size; (3) there exists a trade-off between packing and unpacking when training multiple neural network models on limited resources; (4) a pack-based Hyperband is up to 2.7x faster than the original Hyperband training method in our experiment setting, with this improvement growing as memory size increases and subsequently the density of models packed.

Includes members:
  Member "hypersched dynamic resource reallocation for model development on a deadline":
    also connected through other hyperedges:
      (none)
  Member "crossbow scaling deep learning with small batch sizes on multi gpu servers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimizing multi gpu parallelization strategies for deep learning training")
  Member "3lc lightweight and effective traffic compression for distributed machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "efficient memory management for gpu based deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "streaming convolutional neural networks for end to end learning with multi megapixel images")
  Member "themis fair and efficient gpu cluster scheduling for machine learning workloads":
    also connected through other hyperedges:
      (none)
  Member "exploiting reuse in pipeline aware hyperparameter tuning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "run time parameter sensitivity analysis optimizations")
  Member "analysis of large scale multi tenant gpu clusters for dnn training workloads":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "communication contention aware scheduling of multiple deep learning training jobs")
  Member "energy and policy considerations for deep learning in nlp":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)