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: "accelerating deep convolutional networks using low precision and sparsity"
  Abstract: We explore techniques to significantly improve the compute efficiency and performance of Deep Convolution Networks without impacting their accuracy. To improve the compute efficiency, we focus on achieving high accuracy with extremely low-precision (2-bit) weight networks, and to accelerate the execution time, we aggressively skip operations on zero-values. We achieve the highest reported accuracy of 76.6% Top-1/93% Top-5 on the Imagenet object classification challenge with low-precision network\footnote{github release of the source code coming soon} while reducing the compute requirement by ~3x compared to a full-precision network that achieves similar accuracy. Furthermore, to fully exploit the benefits of our low-precision networks, we build a deep learning accelerator core, dLAC, that can achieve up to 1 TFLOP/mm^2 equivalent for single-precision floating-point operations (~2 TFLOP/mm^2 for half-precision).

Connected through hyperedge HE-1 (induced by source paper "learning efficient convolutional networks through irregular convolutional kernels"):
  with members:
    - "learning efficient convolutional networks through network slimming"
    - "irregular convolutional neural networks"
    - "deformable convolutional networks"
    - "the power of sparsity in convolutional neural networks"
    - "channel pruning for accelerating very deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "ssd single shot multibox detector"
    - "training sparse neural networks"

Connected through hyperedge HE-2 (induced by source paper "instant quantization of neural networks using monte carlo methods"):
  with members:
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "energy and policy considerations for deep learning in nlp"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "training and inference with integers in deep neural networks"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "relaxed quantization for discretized neural networks"
    - "haq hardware aware automated quantization with mixed precision"
    - "adaptive input representations for neural language modeling"

Connected through hyperedge HE-3 (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"
    - "compressing dma engine leveraging activation sparsity for training deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "hadanets flexible quantization strategies for neural networks"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "universal deep neural network compression"
    - "compressing neural networks using the variational information bottleneck"
    - "xcel ram accelerating binary neural networks in high throughput sram compute arrays"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "network sketching exploiting binary structure in deep cnns"
    - "wrpn wide reduced precision networks"

Connected through hyperedge HE-5 (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-6 (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"

Connected through hyperedge HE-7 (induced by source paper "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"):
  with members:
    - "the architectural implications of facebook s dnn based personalized recommendation"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "buddy compression enabling larger memory for deep learning and hpc workloads on gpus"
    - "beyond human level accuracy computational challenges in deep learning"
    - "bit tactical exploiting ineffectual computations in convolutional neural networks which why and how"
    - "competitive analysis system for theatrical movie releases based on movie trailer deep video representation"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-8 (induced by source paper "evolutionary cell aided design for neural network architectures"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"
    - "exploration of low numeric precision deep learning inference using intel fpgas"
    - "large scale evolution of image classifiers"
    - "an analysis of deep neural network models for practical applications"
    - "learning transferable architectures for scalable image recognition"
    - "hierarchical representations for efficient architecture search"
    - "finn a framework for fast scalable binarized neural network inference"

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)