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 "enabling highly efficient capsule networks processing through a pim based architecture design"):
  Abstract: In recent years, the CNNs have achieved great successes in the image processing tasks, e.g., image recognition and object detection. Unfortunately, traditional CNN's classification is found to be easily misled by increasingly complex image features due to the usage of pooling operations, hence unable to preserve accurate position and pose information of the objects. To address this challenge, a novel neural network structure called Capsule Network has been proposed, which introduces equivariance through capsules to significantly enhance the learning ability for image segmentation and object detection. Due to its requirement of performing a high volume of matrix operations, CapsNets have been generally accelerated on modern GPU platforms that provide highly optimized software library for common deep learning tasks. However, based on our performance characterization on modern GPUs, CapsNets exhibit low efficiency due to the special program and execution features of their routing procedure, including massive unshareable intermediate variables and intensive synchronizations, which are very difficult to optimize at software level. To address these challenges, we propose a hybrid computing architecture design named \textit{PIM-CapsNet}. It preserves GPU's on-chip computing capability for accelerating CNN types of layers in CapsNet, while pipelining with an off-chip in-memory acceleration solution that effectively tackles routing procedure's inefficiency by leveraging the processing-in-memory capability of today's 3D stacked memory. Using routing procedure's inherent parallellization feature, our design enables hierarchical improvements on CapsNet inference efficiency through minimizing data movement and maximizing parallel processing in memory.

Includes members:
  Member "optimally scheduling cnn convolutions for efficient memory access":
    also connected through other hyperedges:
      (none)
  Member "capsule neural network based height classification using low cost automotive ultrasonic sensors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capsule network performance with autonomous navigation")
  Member "high throughput cnn inference on embedded arm big little multi core processors":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural network inference on mobile socs")
  Member "beyond the memory wall a case for memory centric hpc system for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "progressive compressed records taking a byte out of deep learning data")
  Member "an overview of deep learning in medical imaging focusing on mri":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "automatic detection and counting of retina cell nuclei using deep learning")
  Member "capsnet comparative performance evaluation for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "capsattacks robust and imperceptible adversarial attacks on capsule networks")
  Member "fast capsnet for lung cancer screening":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "examining the benefits of capsule neural networks")
  Member "origami a heterogeneous split architecture for in memory acceleration of learning":
    also connected through other hyperedges:
      (none)

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.DC(Distributed, Parallel, and Cluster Computing)