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 "semi supervised learning via feedforward designed convolutional neural networks"):
  Abstract: A semi-supervised learning framework using the feedforward-designed convolutional neural networks (FF-CNNs) is proposed for image classification in this work. One unique property of FF-CNNs is that no backpropagation is used in model parameters determination. Since unlabeled data may not always enhance semi-supervised learning, we define an effective quality score and use it to select a subset of unlabeled data in the training process. We conduct experiments on the MNIST, SVHN, and CIFAR-10 datasets, and show that the proposed semi-supervised FF-CNN solution outperforms the CNN trained by backpropagation (BP-CNN) when the amount of labeled data is reduced. Furthermore, we develop an ensemble system that combines the output decision vectors of different semi-supervised FF-CNNs to boost classification accuracy. The ensemble systems can achieve further performance gains on all three benchmarking datasets.

Includes members:
  Member "ensembles of feedforward designed convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cifar 10 image classification using feature ensembles")
  Member "interpretable convolutional neural networks via feedforward design":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models")
  Member "on data driven saak transform":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pointhop a lightweight learning model on point sets for 3d classification")
  Member "a saak transform approach to efficient scalable and robust handwritten digits recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pointhop a lightweight learning model on point sets for 3d classification")
  Member "good semi supervised learning that requires a bad gan":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial code learning for image generation")
  Member "interpretable convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convolutional occupancy networks")
  Member "improved techniques for training gans":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "understanding convolutional neural networks with a mathematical model":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models")

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)