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: "generalized capsule networks with trainable routing procedure"
  Abstract: CapsNet (Capsule Network) was first proposed by~\citet{capsule} and later another version of CapsNet was proposed by~\citet{emrouting}. CapsNet has been proved effective in modeling spatial features with much fewer parameters. However, the routing procedures in both papers are not well incorporated into the whole training process. The optimal number of routing procedure is misery which has to be found manually. To overcome this disadvantages of current routing procedures in CapsNet, we embed the routing procedure into the optimization procedure with all other parameters in neural networks, namely, make coupling coefficients in the routing procedure become completely trainable. We call it Generalized CapsNet (G-CapsNet). We implement both "full-connected" version of G-CapsNet and "convolutional" version of G-CapsNet. G-CapsNet achieves a similar performance in the dataset MNIST as in the original papers. We also test two capsule packing method (cross feature maps or with feature maps) from previous convolutional layers and see no evident difference. Besides, we also explored possibility of stacking multiple capsule layers. The code is shared on \hyperlink{this https URL}{CAFFE-CapsNet}.

Connected through hyperedge HE-1 (induced by source paper "homogeneous vector capsules enable adaptive gradient descent in convolutional neural networks"):
  with members:
    - "effects of degradations on deep neural network architectures"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "an artificial neural network architecture based on context transformations in cortical minicolumns"
    - "snapshot ensembles train 1 get m for free"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dynamic routing between capsules"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-2 (induced by source paper "p capsnets a general form of convolutional neural networks"):
  with members:
    - "capsule routing via variational bayes"
    - "attention routing between capsules"
    - "capsule networks need an improved routing algorithm"
    - "neural network encapsulation"
    - "fast dynamic routing based on weighted kernel density estimation"
    - "prodsumnet reducing model parameters in deep neural networks via product of sums matrix decompositions"
    - "dense and diverse capsule networks making the capsules learn better"
    - "dynamic routing between capsules"

Connected through hyperedge HE-3 (induced by source paper "improving the robustness of capsule networks to image affine transformations"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "attention routing between capsules"
    - "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-4 (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"
    - "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"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"

Connected through hyperedge HE-5 (induced by source paper "capsule networks for brain tumor classification based on mri images and course tumor boundaries"):
  with members:
    - "fast capsnet for lung cancer screening"
    - "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)