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: "dense and diverse capsule networks making the capsules learn better"
  Abstract: Past few years have witnessed exponential growth of interest in deep learning methodologies with rapidly improving accuracies and reduced computational complexity. In particular, architectures using Convolutional Neural Networks (CNNs) have produced state-of-the-art performances for image classification and object recognition tasks. Recently, Capsule Networks (CapsNet) achieved significant increase in performance by addressing an inherent limitation of CNNs in encoding pose and deformation. Inspired by such advancement, we asked ourselves, can we do better? We propose Dense Capsule Networks (DCNet) and Diverse Capsule Networks (DCNet++). The two proposed frameworks customize the CapsNet by replacing the standard convolutional layers with densely connected convolutions. This helps in incorporating feature maps learned by different layers in forming the primary capsules. DCNet, essentially adds a deeper convolution network, which leads to learning of discriminative feature maps. Additionally, DCNet++ uses a hierarchical architecture to learn capsules that represent spatial information in a fine-to-coarser manner, which makes it more efficient for learning complex data. Experiments on image classification task using benchmark datasets demonstrate the efficacy of the proposed architectures. DCNet achieves state-of-the-art performance (99.75%) on MNIST dataset with twenty fold decrease in total training iterations, over the conventional CapsNet. Furthermore, DCNet++ performs better than CapsNet on SVHN dataset (96.90%), and outperforms the ensemble of seven CapsNet models on CIFAR-10 by 0.31% with seven fold decrease in number of parameters.

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

Connected through hyperedge HE-2 (induced by source paper "a review of modularization techniques in artificial neural networks"):
  with members:
    - "knowledge transfer in deep block modular neural networks"
    - "graph hypernetworks for neural architecture search"
    - "mattnet modular attention network for referring expression comprehension"
    - "relational inductive biases deep learning and graph networks"
    - "a graph to sequence model for amr to text generation"
    - "evolving deep neural networks"
    - "hierarchical semantic segmentation using modular convolutional neural networks"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-3 (induced by source paper "grouping capsules based different types"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "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"

Connected through hyperedge HE-4 (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-5 (induced by source paper "building deep equivariant capsule networks"):
  with members:
    - "deepcaps going deeper with capsule networks"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "self attention capsule networks for object classification"):
  with members:
    - "quantitative phase imaging and artificial intelligence a review"
    - "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"
    - "attention u net learning where to look for the pancreas"
    - "non local neural networks"
    - "dynamic routing between capsules"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "capsule routing via variational bayes"):
  with members:
    - "an effective hit or miss layer favoring feature interpretation as learned prototypes deformations"
    - "attention routing between capsules"
    - "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"

Connected through hyperedge HE-8 (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"
    - "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"
    - "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)