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: "attention routing between capsules"
  Abstract: In this paper, we propose a new capsule network architecture called Attention Routing CapsuleNet (AR CapsNet). We replace the dynamic routing and squash activation function of the capsule network with dynamic routing (CapsuleNet) with the attention routing and capsule activation. The attention routing is a routing between capsules through an attention module. The attention routing is a fast forward-pass while keeping spatial information. On the other hand, the intuitive interpretation of the dynamic routing is finding a centroid of the prediction capsules. Thus, the squash activation function and its variant focus on preserving a vector orientation. However, the capsule activation focuses on performing a capsule-scale activation function. #R##N#We evaluate our proposed model on the MNIST, affNIST, and CIFAR-10 classification tasks. The proposed model achieves higher accuracy with fewer parameters (x0.41 parameters in the MNIST, x0.45 parameters in the CIFAR-10) and less training time than CapsuleNet (x0.40 training time in the MNIST, x0.30 training time in the CIFAR-10). These results validate that designing a capsule-scale operation is a key factor to implement the capsule concept. #R##N#Also, our experiment shows that our proposed model is transformation equivariant as CapsuleNet. As we perturb each element of the output capsule, the decoder attached to the output capsules shows global variations. Further experiments show that the difference in the capsule features caused by applying affine transformations on an input image is significantly aligned in one direction.

Connected through hyperedge HE-1 (induced by source paper "capsules with inverted dot product attention routing"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "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 "building deep equivariant capsule networks"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "equivariant transformer networks"
    - "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-3 (induced by source paper "capsule routing via variational bayes"):
  with members:
    - "an effective hit or miss layer favoring feature interpretation as learned prototypes deformations"
    - "capsule networks with max min normalization"
    - "the multi lane capsule network mlcn"
    - "videocapsulenet a simplified network for action detection"
    - "cappronet deep feature learning via orthogonal projections onto capsule subspaces"
    - "group equivariant capsule networks"
    - "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 "p capsnets a general form of convolutional neural networks"):
  with members:
    - "capsule routing via variational bayes"
    - "capsule networks need an improved routing algorithm"
    - "generalized capsule networks with trainable routing procedure"
    - "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-5 (induced by source paper "improving the robustness of capsule networks to image affine transformations"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "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"

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)