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: "capsule networks against medical imaging data challenges"
  Abstract: A key component to the success of deep learning is the availability of massive amounts of training data. Building and annotating large datasets for solving medical image classification problems is today a bottleneck for many applications. Recently, capsule networks were proposed to deal with shortcomings of Convolutional Neural Networks (ConvNets). In this work, we compare the behavior of capsule networks against ConvNets under typical datasets constraints of medical image analysis, namely, small amounts of annotated data and class-imbalance. We evaluate our experiments on MNIST, Fashion-MNIST and medical (histological and retina images) publicly available datasets. Our results suggest that capsule networks can be trained with less amount of data for the same or better performance and are more robust to an imbalanced class distribution, which makes our approach very promising for the medical imaging community.

Connected through hyperedge HE-1 (induced by source paper "examining the benefits of capsule neural networks"):
  with members:
    - "convolutional capsule network for classification of breast cancer histology images"
    - "videocapsulenet a simplified network for action detection"
    - "fast capsnet for lung cancer screening"
    - "brain tumor type classification via capsule networks"
    - "improved explainability of capsule networks relevance path by agreement"
    - "novel deep learning model for traffic sign detection using capsule networks"
    - "deep reinforcement learning using capsules in advanced game environments"
    - "compositional coding capsule network with k means routing for text classification"

Connected through hyperedge HE-2 (induced by source paper "hierarchical taxonomy aware and attentional graph capsule rcnns for large scale multi label text classification"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "sgm sequence generation model for multi label classification"
    - "graph convolutional networks for text classification"
    - "attention based capsule networks with dynamic routing for relation extraction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dynamic routing between capsules"
    - "adversarial multi task learning for text classification"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-3 (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"
    - "capsnet comparative performance evaluation for image classification"
    - "fast capsnet for lung cancer screening"
    - "origami a heterogeneous split architecture for in memory acceleration of learning"

Connected through hyperedge HE-4 (induced by source paper "efficiency and scalability of multi lane capsule networks mlcn"):
  with members:
    - "path capsule networks"
    - "capsnet comparative performance evaluation for image classification"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "random capsnet forest model for imbalanced malware type classification task"):
  with members:
    - "convolutional capsule network for classification of breast cancer histology images"
    - "deceiving end to end deep learning malware detectors using adversarial examples"
    - "capsacc an efficient hardware accelerator for capsulenets with data reuse"
    - "fast capsnet for lung cancer screening"
    - "deep transfer learning for static malware classification"
    - "brain tumor type classification via capsule networks"
    - "microsoft malware classification challenge"
    - "deep learning at the shallow end malware classification for non domain experts"

Connected through hyperedge HE-6 (induced by source paper "the multi lane capsule network mlcn"):
  with members:
    - "path capsule networks"
    - "capsnet comparative performance evaluation for image classification"
    - "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"
    - "dynamic routing between capsules"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-7 (induced by source paper "3d point capsule networks"):
  with members:
    - "indoor scene understanding in 2 5 3d for autonomous agents a survey"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "generalized capsule networks with trainable routing procedure"
    - "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"

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)