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: "tflms large model support in tensorflow by graph rewriting"
  Abstract: While accelerators such as GPUs have limited memory, deep neural networks are becoming larger and will not fit with the memory limitation of accelerators for training. We propose an approach to tackle this problem by rewriting the computational graph of a neural network, in which swap-out and swap-in operations are inserted to temporarily store intermediate results on CPU memory. In particular, we first revise the concept of a computational graph by defining a concrete semantics for variables in a graph. We then formally show how to derive swap-out and swap-in operations from an existing graph and present rules to optimize the graph. To realize our approach, we developed a module in TensorFlow, named TFLMS. TFLMS is published as a pull request in the TensorFlow repository for contributing to the TensorFlow community. With TFLMS, we were able to train ResNet-50 and 3DUnet with 4.7x and 2x larger batch size, respectively. In particular, we were able to train 3DUNet using images of size of $192^3$ for image segmentation, which, without TFLMS, had been done only by dividing the images to smaller images, which affects the accuracy.

Connected through hyperedge HE-1 (induced by source paper "understanding and optimizing packed neural network training for hyper parameter tuning"):
  with members:
    - "hypersched dynamic resource reallocation for model development on a deadline"
    - "crossbow scaling deep learning with small batch sizes on multi gpu servers"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "efficient memory management for gpu based deep learning systems"
    - "themis fair and efficient gpu cluster scheduling for machine learning workloads"
    - "exploiting reuse in pipeline aware hyperparameter tuning"
    - "analysis of large scale multi tenant gpu clusters for dnn training workloads"
    - "energy and policy considerations for deep learning in nlp"

Connected through hyperedge HE-2 (induced by source paper "accudnn a gpu memory efficient accelerator for training ultra deep neural networks"):
  with members:
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "don t decay the learning rate increase the batch size"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "training deep nets with sublinear memory cost"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "fast and accurate 3d medical image segmentation with data swapping method"):
  with members:
    - "two stage convolutional neural network for breast cancer histology image classification"
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "brain tumor segmentation with deep neural networks"
    - "a survey on deep learning in medical image analysis"
    - "instance normalization the missing ingredient for fast stylization"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

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)