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 "p capsnets a general form of convolutional neural networks"):
  Abstract: We propose Pure CapsNets (P-CapsNets) which is a generation of normal CNNs structurally. Specifically, we make three modifications to current CapsNets. First, we remove routing procedures from CapsNets based on the observation that the coupling coefficients can be learned implicitly. Second, we replace the convolutional layers in CapsNets to improve efficiency. Third, we package the capsules into rank-3 tensors to further improve efficiency. The experiment shows that P-CapsNets achieve better performance than CapsNets with varied routing procedures by using significantly fewer parameters on MNIST\&CIFAR10. The high efficiency of P-CapsNets is even comparable to some deep compressing models. For example, we achieve more than 99\% percent accuracy on MNIST by using only 3888 parameters. We visualize the capsules as well as the corresponding correlation matrix to show a possible way of initializing CapsNets in the future. We also explore the adversarial robustness of P-CapsNets compared to CNNs.

Includes members:
  Member "capsule routing via variational bayes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi source deep domain adaptation for quality control in retail food packaging")
  Member "attention routing between capsules":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capsules with inverted dot product attention routing")
  Member "capsule networks need an improved routing algorithm":
    also connected through other hyperedges:
      (none)
  Member "generalized capsule networks with trainable routing procedure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "homogeneous vector capsules enable adaptive gradient descent in convolutional neural networks")
  Member "neural network encapsulation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capsules with inverted dot product attention routing")
  Member "fast dynamic routing based on weighted kernel density estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "grouping capsules based different types")
  Member "prodsumnet reducing model parameters in deep neural networks via product of sums matrix decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multiple style transfer in real time")
  Member "dense and diverse capsule networks making the capsules learn better":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capsules with inverted dot product attention routing")

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)