human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "origami a 803 gop s w convolutional network accelerator":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pulp nn accelerating quantized neural networks on parallel ultra low power risc v processors")
  Member "a near threshold risc v core with dsp extensions for scalable iot endpoint devices":
    also connected through other hyperedges:
      (none)
  Member "deep neural networks are robust to weight binarization and other non linear distortions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources")
  Member "identity mappings in deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "eie efficient inference engine on compressed deep neural network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cluster pruning an efficient filter pruning method for edge ai vision applications")
  Member "acoustic scene classification using convolutional neural network and multiple width frequency delta data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural architecture search on acoustic scene classification")
  Member "fathom reference workloads for modern deep learning methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "a taxonomy of deep convolutional neural nets for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pi net a deep learning approach to extract topological persistence images")

Question: Please tell me which class the source paper 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)