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: "deepcaps going deeper with capsule networks"
  Abstract: Capsule Network is a promising concept in deep learning, yet its true potential is not fully realized thus far, providing sub-par performance on several key benchmark datasets with complex data. Drawing intuition from the success achieved by Convolutional Neural Networks (CNNs) by going deeper, we introduce DeepCaps1, a deep capsule network architecture which uses a novel 3D convolution based dynamic routing algorithm. With DeepCaps, we surpass the state-of-the-art results in the capsule network domain on CIFAR10, SVHN and Fashion MNIST, while achieving a 68% reduction in the number of parameters. Further, we propose a class-independent decoder network, which strengthens the use of reconstruction loss as a regularization term. This leads to an interesting property of the decoder, which allows us to identify and control the physical attributes of the images represented by the instantiation parameters.

Connected through hyperedge HE-1 (induced by source paper "capsules with inverted dot product attention routing"):
  with members:
    - "attention routing between capsules"
    - "deep equilibrium models"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "neural network encapsulation"
    - "dense and diverse capsule networks making the capsules learn better"
    - "attention is all you need"
    - "dynamic routing between capsules"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "detecting and diagnosing adversarial images with class conditional capsule reconstructions"):
  with members:
    - "mnist c a robustness benchmark for computer vision"
    - "are odds really odd bypassing statistical detection of adversarial examples"
    - "on the vulnerability of capsule networks to adversarial attacks"
    - "semantic image synthesis with spatially adaptive normalization"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "adversarial attacks and defences competition"
    - "towards the first adversarially robust neural network model on mnist"
    - "adversarial spheres"

Connected through hyperedge HE-3 (induced by source paper "grouping capsules based different types"):
  with members:
    - "capsule routing via variational bayes"
    - "assessing capsule networks with biased data"
    - "the multi lane capsule network mlcn"
    - "deep learning for classical japanese literature"
    - "training deep capsule networks"
    - "hitnet a neural network with capsules embedded in a hit or miss layer extended with hybrid data augmentation and ghost capsules"
    - "fast dynamic routing based on weighted kernel density estimation"
    - "dense and diverse capsule networks making the capsules learn better"

Connected through hyperedge HE-4 (induced by source paper "building deep equivariant capsule networks"):
  with members:
    - "equivariant transformer networks"
    - "attention routing between capsules"
    - "the multi lane capsule network mlcn"
    - "context aware multipath networks"
    - "deep learning for classical japanese literature"
    - "group equivariant capsule networks"
    - "brain tumor type classification via capsule networks"
    - "hitnet a neural network with capsules embedded in a hit or miss layer extended with hybrid data augmentation and ghost capsules"

Connected through hyperedge HE-5 (induced by source paper "improving the robustness of capsule networks to image affine transformations"):
  with members:
    - "attention routing between capsules"
    - "generalized capsule networks with trainable routing procedure"
    - "neural network encapsulation"
    - "group equivariant capsule networks"
    - "fast dynamic routing based on weighted kernel density estimation"
    - "sparse unsupervised capsules generalize better"
    - "capsgan using dynamic routing for generative adversarial networks"
    - "dynamic routing between capsules"

Connected through hyperedge HE-6 (induced by source paper "red cane a systematic methodology for resilience analysis and design of capsule networks under approximations"):
  with members:
    - "autoax an automatic design space exploration and circuit building methodology utilizing libraries of approximate components"
    - "alwann automatic layer wise approximation of deep neural network accelerators without retraining"
    - "towards scalable and reliable capsule networks for challenging nlp applications"
    - "capsacc an efficient hardware accelerator for capsulenets with data reuse"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "dynamic routing between capsules"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "significance driven hybrid 8t 6t sram for energy efficient synaptic storage in artificial neural networks"

Connected through hyperedge HE-7 (induced by source paper "increasing the adversarial robustness and explainability of capsule networks with gamma capsules"):
  with members:
    - "on the vulnerability of capsule networks to adversarial attacks"
    - "detecting and diagnosing adversarial images with class conditional capsule reconstructions"
    - "3d point capsule networks"
    - "videocapsulenet a simplified network for action detection"
    - "fast capsnet for lung cancer screening"
    - "dynamic routing between capsules"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-8 (induced by source paper "adaptive routing between capsules"):
  with members:
    - "cappronet deep feature learning via orthogonal projections onto capsule subspaces"
    - "neural network encapsulation"
    - "group equivariant capsule networks"
    - "dynamic routing between capsules"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale 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.CV(Computer Vision and Pattern Recognition)