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 "the multi lane capsule network mlcn"):
  Abstract: We introduce Multi-Lane Capsule Networks (MLCN), which are a separable and resource efficient organization of Capsule Networks (CapsNet) that allows parallel processing, while achieving high accuracy at reduced cost. A MLCN is composed of a number of (distinct) parallel lanes, each contributing to a dimension of the result, trained using the routing-by-agreement organization of CapsNet. Our results indicate similar accuracy with a much reduced cost in number of parameters for the Fashion-MNIST and Cifar10 datsets. They also indicate that the MLCN outperforms the original CapsNet when using a proposed novel configuration for the lanes. MLCN also has faster training and inference times, being more than two-fold faster than the original CapsNet in the same accelerator.

Includes members:
  Member "path capsule networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a tutorial on ensembles and deep learning fusion with mnist as guiding thread a complex heterogeneous fusion scheme reaching 10 digits error")
  Member "capsnet comparative performance evaluation for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "enabling highly efficient capsule networks processing through a pim based architecture design")
  Member "fast capsnet for lung cancer screening":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "examining the benefits of capsule neural networks")
  Member "improved explainability of capsule networks relevance path by agreement":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "examining the benefits of capsule neural networks")
  Member "compositional coding capsule network with k means routing for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "examining the benefits of capsule neural networks")
  Member "capsule networks against medical imaging data challenges":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "examining the benefits of capsule neural networks")
  Member "dynamic routing between capsules":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lightweight residual densely connected convolutional neural network")
  Member "fashion mnist a novel image dataset for benchmarking machine learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)