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: "neurostream scalable and energy efficient deep learning with smart memory cubes"
  Abstract: High-performance computing systems are moving towards 2.5D and 3D memory hierarchies, based on High Bandwidth Memory (HBM) and Hybrid Memory Cube (HMC) to mitigate the main memory bottlenecks. This trend is also creating new opportunities to revisit near-memory computation. In this paper, we propose a flexible processor-in-memory (PIM) solution for scalable and energy-efficient execution of deep convolutional networks (ConvNets), one of the fastest-growing workloads for servers and high-end embedded systems. Our co-design approach consists of a network of Smart Memory Cubes (modular extensions to the standard HMC) each augmented with a many-core PIM platform called NeuroCluster. NeuroClusters have a modular design based on NeuroStream coprocessors (for Convolution-intensive computations) and general-purpose RISC-V cores. In addition, a DRAM-friendly tiling mechanism and a scalable computation paradigm are presented to efficiently harness this computational capability with a very low programming effort. NeuroCluster occupies only 8 percent of the total logic-base (LoB) die area in a standard HMC and achieves an average performance of 240 GFLOPS for complete execution of full-featured state-of-the-art (SoA) ConvNets within a power budget of 2.5 W. Overall 11 W is consumed in a single SMC device, with 22.5 GFLOPS/W energy-efficiency which is 3.5X better than the best GPU implementations in similar technologies. The minor increase in system-level power and the negligible area increase make our PIM system a cost-effective and energy efficient solution, easily scalable to 955 GFLOPS with a small network of just four SMCs.

Connected through hyperedge HE-1 (induced by source paper "a unified optimization approach for cnn model inference on integrated gpus"):
  with members:
    - "inference of quantized neural networks on heterogeneous all programmable devices"
    - "yolov3 an incremental improvement"
    - "learning to optimize tensor programs"
    - "optimizing cnn model inference on cpus"
    - "mnasnet platform aware neural architecture search for mobile"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "embedded binarized neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "optimally scheduling cnn convolutions for efficient memory access"):
  with members:
    - "xnor neural engine a hardware accelerator ip for 21 6 fj op binary neural network inference"
    - "extended bit plane compression for convolutional neural network accelerators"
    - "an iot endpoint system on chip for secure and energy efficient near sensor analytics"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "the power of sparsity in convolutional neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "a systematic approach to blocking convolutional neural networks"
    - "ristretto hardware oriented approximation of convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "an application specific vliw processor with vector instruction set for cnn acceleration"):
  with members:
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "origami a 803 gop s w convolutional network accelerator"
    - "energy efficient convnets through approximate computing"
    - "fixed point quantization of deep convolutional networks"
    - "you only look once unified real time object detection"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "neura ghe exploiting cpu fpga synergies for efficient and flexible cnn inference acceleration on zynq socs"):
  with members:
    - "squeezejet high level synthesis accelerator design for deep convolutional neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "an iot endpoint system on chip for secure and energy efficient near sensor analytics"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "origami a 803 gop s w convolutional network accelerator"
    - "finn a framework for fast scalable binarized neural network inference"
    - "dialog based language learning"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-5 (induced by source paper "memory slices a modular building block for scalable intelligent memory systems"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "an analysis of deep neural network models for practical applications"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "a scalable near memory architecture for training deep neural networks on large in memory datasets"):
  with members:
    - "efficient processing of deep neural networks a tutorial and survey"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "communication efficient learning of deep networks from decentralized data"
    - "optimizing performance of recurrent neural networks on gpus"
    - "deep image scaling up image recognition"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-7 (induced by source paper "ntx an energy efficient streaming accelerator for floating point generalized reduction workloads in 22nm fd soi"):
  with members:
    - "a scalable near memory architecture for training deep neural networks on large in memory datasets"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-8 (induced by source paper "snowflake a model agnostic accelerator for convolutional neural networks"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "very deep convolutional networks for text classification"
    - "eie efficient inference engine on compressed deep neural network"
    - "wider or deeper revisiting the resnet model for visual recognition"
    - "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"

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.AR(Hardware Architecture)