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: "capsnet comparative performance evaluation for image classification"
  Abstract: Image classification has become one of the main tasks in the field of computer vision technologies. In this context, a recent algorithm called CapsNet that implements an approach based on activity vectors and dynamic routing between capsules may overcome some of the limitations of the current state of the art artificial neural networks (ANN) classifiers, such as convolutional neural networks (CNN). In this paper, we evaluated the performance of the CapsNet algorithm in comparison with three well-known classifiers (Fisher-faces, LeNet, and ResNet). We tested the classification accuracy on four datasets with a different number of instances and classes, including images of faces, traffic signs, and everyday objects. The evaluation results show that even for simple architectures, training the CapsNet algorithm requires significant computational resources and its classification performance falls below the average accuracy values of the other three classifiers. However, we argue that CapsNet seems to be a promising new technique for image classification, and further experiments using more robust computation resources and re-fined CapsNet architectures may produce better outcomes.

Connected through hyperedge HE-1 (induced by source paper "enabling highly efficient capsule networks processing through a pim based architecture design"):
  with members:
    - "optimally scheduling cnn convolutions for efficient memory access"
    - "capsule neural network based height classification using low cost automotive ultrasonic sensors"
    - "high throughput cnn inference on embedded arm big little multi core processors"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "an overview of deep learning in medical imaging focusing on mri"
    - "fast capsnet for lung cancer screening"
    - "origami a heterogeneous split architecture for in memory acceleration of learning"
    - "novel deep learning model for traffic sign detection using capsule networks"

Connected through hyperedge HE-2 (induced by source paper "capsattacks robust and imperceptible adversarial attacks on capsule networks"):
  with members:
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "novel deep learning model for traffic sign detection using capsule networks"
    - "darccc detecting adversaries by reconstruction from class conditional capsules"
    - "are adversarial examples inevitable"
    - "dolphinattack inaudible voice commands"
    - "enhancing robustness of machine learning systems via data transformations"
    - "dynamic routing between capsules"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "efficiency and scalability of multi lane capsule networks mlcn"):
  with members:
    - "path capsule networks"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "beyond data and model parallelism for deep neural networks"
    - "fast capsnet for lung cancer screening"
    - "improved explainability of capsule networks relevance path by agreement"
    - "compositional coding capsule network with k means routing for text classification"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "a capsule network for traffic speed prediction in complex road networks"

Connected through hyperedge HE-4 (induced by source paper "capsule networks need an improved routing algorithm"):
  with members:
    - "an attention based bi gru capsnet model for hypernymy detection between compound entities"
    - "videocapsulenet a simplified network for action detection"
    - "cappronet deep feature learning via orthogonal projections onto capsule subspaces"
    - "fast capsnet for lung cancer screening"
    - "group equivariant capsule networks"
    - "deep reinforcement learning using capsules in advanced game environments"
    - "sparse unsupervised capsules generalize better"
    - "dynamic routing between capsules"

Connected through hyperedge HE-5 (induced by source paper "the multi lane capsule network mlcn"):
  with members:
    - "path capsule networks"
    - "fast capsnet for lung cancer screening"
    - "improved explainability of capsule networks relevance path by agreement"
    - "compositional coding capsule network with k means routing for text classification"
    - "capsule networks against medical imaging data challenges"
    - "dynamic routing between capsules"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-6 (induced by source paper "an efficient approach for using expectation maximization algorithm in capsule networks"):
  with members:
    - "avoiding implementation pitfalls of matrix capsules with em routing by hinton et al"
    - "dynamic routing between capsules"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "adam a method for stochastic optimization"

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.CV(Computer Vision and Pattern Recognition)