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: "compressing dma engine leveraging activation sparsity for training deep neural networks"
  Abstract: Popular deep learning frameworks require users to fine-tune their memory usage so that the training data of a deep neural network (DNN) fits within the GPU physical memory. Prior work tries to address this restriction by virtualizing the memory usage of DNNs, enabling both CPU and GPU memory to be utilized for memory allocations. Despite its merits, virtualizing memory can incur significant performance overheads when the time needed to copy data back and forth from CPU memory is higher than the latency to perform the computations required for DNN forward and backward propagation. We introduce a high-performance virtualization strategy based on a "compressing DMA engine" (cDMA) that drastically reduces the size of the data structures that are targeted for CPU-side allocations. The cDMA engine offers an average 2.6x (maximum 13.8x) compression ratio by exploiting the sparsity inherent in offloaded data, improving the performance of virtualized DNNs by an average 32% (maximum 61%).

Connected through hyperedge HE-1 (induced by source paper "smartphone impostor detection with built in sensors and deep learning"):
  with members:
    - "implicit smartphone user authentication with sensors and contextual machine learning"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-2 (induced by source paper "low memory neural network training a technical report"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "pay less attention with lightweight and dynamic convolutions"
    - "learning fast algorithms for linear transforms using butterfly factorizations"
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "memory efficient adaptive optimization"
    - "sparse evolutionary deep learning with over one million artificial neurons on commodity hardware"
    - "fixup initialization residual learning without normalization"
    - "full deep neural network training on a pruned weight budget"

Connected through hyperedge HE-3 (induced by source paper "neural network model extraction attacks in edge devices by hearing architectural hints"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "tvm end to end optimization stack for deep learning"
    - "i know what you see power side channel attack on convolutional neural network accelerators"
    - "sok security and privacy in machine learning"
    - "stealing hyperparameters in machine learning"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-4 (induced by source paper "spring a sparsity aware reduced precision monolithic 3d cnn accelerator architecture for training and inference"):
  with members:
    - "software defined design space exploration for an efficient ai accelerator architecture"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "image classification at supercomputer scale"
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "progressive neural architecture search"

Connected through hyperedge HE-5 (induced by source paper "beyond the memory wall a case for memory centric hpc system for deep learning"):
  with members:
    - "bit tactical exploiting ineffectual computations in convolutional neural networks which why and how"
    - "on scale out deep learning training for cloud and hpc"
    - "tbd benchmarking and analyzing deep neural network training"
    - "imagenet training in minutes"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "a read write memory network for movie story understanding"
    - "deep learning for video classification and captioning"

Connected through hyperedge HE-6 (induced by source paper "prema a predictive multi task scheduling algorithm for preemptible neural processing units"):
  with members:
    - "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "beyond human level accuracy computational challenges in deep learning"
    - "scale sim systolic cnn accelerator simulator"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-7 (induced by source paper "dasnet dynamic activation sparsity for neural network efficiency improvement"):
  with members:
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "channel gating neural networks"
    - "dynamic channel pruning feature boosting and suppression"
    - "network dissection quantifying interpretability of deep visual representations"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning efficient convolutional networks through network slimming"
    - "data driven sparse structure selection for deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-8 (induced by source paper "neummu architectural support for efficient address translations in neural processing units"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "prema a predictive multi task scheduling algorithm for preemptible neural processing units"
    - "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "beyond human level accuracy computational challenges in deep learning"
    - "bit tactical exploiting ineffectual computations in convolutional neural networks which why and how"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"

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)