human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "grouping capsules based different types"):
  Abstract: Capsule network was introduced as a new architecture of neural networks, it encoding features as capsules to overcome the lacking of equivariant in the convolutional neural networks. It uses dynamic routing algorithm to train parameters in different capsule layers, but the dynamic routing algorithm need to be improved. In this paper, we propose a novel capsule network architecture and discussed the effect of initialization method of the coupling coefficient $c_{ij}$ on the model. First, we analyze the rate of change of the initial value of $c_{ij}$ when the dynamic routing algorithm iterates. The larger the initial value of $c_{ij}$, the better effect of the model. Then, we proposed improvement that training different types of capsules by grouping capsules based different types. And this improvement can adjust the initial value of $c_{ij}$ to make it more suitable. We experimented with our improvements on some computer vision datasets and achieved better results than the original capsule network

Includes members:
  Member "deepcaps going deeper with capsule networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capsules with inverted dot product attention routing")
  Member "capsule routing via variational bayes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi source deep domain adaptation for quality control in retail food packaging")
  Member "assessing capsule networks with biased data":
    also connected through other hyperedges:
      (none)
  Member "the multi lane capsule network mlcn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "building deep equivariant capsule networks")
  Member "deep learning for classical japanese literature":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improve sgd training via aligning min batches")
  Member "training deep capsule networks":
    also connected through other hyperedges:
      (none)
  Member "hitnet a neural network with capsules embedded in a hit or miss layer extended with hybrid data augmentation and ghost capsules":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a context aware loss function for action spotting in soccer videos")
  Member "fast dynamic routing based on weighted kernel density estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards scalable and reliable capsule networks for challenging nlp applications")

Question: Please tell me which class the source paper 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)