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: "superneurons dynamic gpu memory management for training deep neural networks"
  Abstract: Going deeper and wider in neural architectures improves their accuracy, while the limited GPU DRAM places an undesired restriction on the network design domain. Deep Learning (DL) practitioners either need to change to less desired network architectures, or nontrivially dissect a network across multiGPUs. These distract DL practitioners from concentrating on their original machine learning tasks. We present SuperNeurons: a dynamic GPU memory scheduling runtime to enable the network training far beyond the GPU DRAM capacity. SuperNeurons features 3 memory optimizations, Liveness Analysis, Unified Tensor Pool, and Cost-Aware Recomputation; together they effectively reduce the network-wide peak memory usage down to the maximal memory usage among layers. We also address the performance issues in these memory-saving techniques. Given the limited GPU DRAM, SuperNeurons not only provisions the necessary memory for the training, but also dynamically allocates the memory for convolution workspaces to achieve the high performance. Evaluations against Caffe, Torch, MXNet and TensorFlow have demonstrated that SuperNeurons trains at least 3.2432 deeper network than current ones with the leading performance. Particularly, SuperNeurons can train ResNet2500 that has 104 basic network layers on a 12GB K40c.

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 "evaluating modern gpu interconnect pcie nvlink nv sli nvswitch and gpudirect"):
  with members:
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "optimized broadcast for deep learning workloads on dense gpu infiniband clusters mpi or nccl"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-3 (induced by source paper "wireless edge computing with latency and reliability guarantees"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "how reliable and capable is multi connectivity"
    - "a tutorial on beam management for 3gpp nr at mmwave frequencies"
    - "uav communications for 5g and beyond recent advances and future trends"
    - "coded federated computing in wireless networks with straggling devices and imperfect csi"
    - "ultra reliable communication in 5g mmwave networks a risk sensitive approach"
    - "on device federated learning via blockchain and its latency analysis"

Connected through hyperedge HE-4 (induced by source paper "crossbow scaling deep learning with small batch sizes on multi gpu servers"):
  with members:
    - "three factors influencing minima in sgd"
    - "revisiting small batch training for deep neural networks"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "measuring the effects of data parallelism on neural network training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "ray a distributed framework for emerging ai applications"
    - "scaling sgd batch size to 32k for imagenet training"

Connected through hyperedge HE-5 (induced by source paper "improving strong scaling of cnn training by exploiting finer grained parallelism"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "communication optimal convolutional neural nets"
    - "exploring the limits of weakly supervised pretraining"
    - "measuring the effects of data parallelism on neural network training"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "imagenet training in minutes"
    - "an analysis of deep neural network models for practical applications"
    - "integrated model batch and domain parallelism in training neural networks"

Connected through hyperedge HE-6 (induced by source paper "deepsz a novel framework to compress deep neural networks by using error bounded lossy compression"):
  with members:
    - "optimizing lossy compression rate distortion from automatic online selection between sz and zfp"
    - "significantly improving lossy compression for scientific data sets based on multidimensional prediction and error controlled quantization"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "in depth exploration of single snapshot lossy compression techniques for n body simulations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"
    - "compressing neural networks with the hashing trick"

Connected through hyperedge HE-7 (induced by source paper "efficient memory management for gpu based deep learning systems"):
  with members:
    - "training wide residual networks for deployment using a single bit for each weight"
    - "ease ml towards multi tenant resource sharing for machine learning workloads"
    - "memory efficient implementation of densenets"
    - "comparative study of deep learning software frameworks"
    - "lifetime based memory management for distributed data processing systems"
    - "database meets deep learning challenges and opportunities"
    - "eie efficient inference engine on compressed deep neural network"
    - "training deep nets with sublinear memory cost"

Connected through hyperedge HE-8 (induced by source paper "a graph theoretic framework of recomputation algorithms for memory efficient backpropagation"):
  with members:
    - "thinet a filter level pruning method for deep neural network compression"
    - "memory efficient implementation of densenets"
    - "megdet a large mini batch object detector"
    - "mixed precision training"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "pyramid scene parsing network"
    - "training deep nets with sublinear memory cost"
    - "u net convolutional networks for biomedical 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.DC(Distributed, Parallel, and Cluster Computing)