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 "hitnet a neural network with capsules embedded in a hit or miss layer extended with hybrid data augmentation and ghost capsules"):
  Abstract: Neural networks designed for the task of classification have become a commodity in recent years. Many works target the development of better networks, which results in a complexification of their architectures with more layers, multiple sub-networks, or even the combination of multiple classifiers. In this paper, we show how to redesign a simple network to reach excellent performances, which are better than the results reproduced with CapsNet on several datasets, by replacing a layer with a Hit-or-Miss layer. This layer contains activated vectors, called capsules, that we train to hit or miss a central capsule by tailoring a specific centripetal loss function. We also show how our network, named HitNet, is capable of synthesizing a representative sample of the images of a given class by including a reconstruction network. This possibility allows to develop a data augmentation step combining information from the data space and the feature space, resulting in a hybrid data augmentation process. In addition, we introduce the possibility for HitNet, to adopt an alternative to the true target when needed by using the new concept of ghost capsules, which is used here to detect potentially mislabeled images in the training data.

Includes members:
  Member "object localization and motion transfer learning with capsules":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an effective hit or miss layer favoring feature interpretation as learned prototypes deformations")
  Member "an attention based bi gru capsnet model for hypernymy detection between compound entities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "question answering based clinical text structuring using pre trained language model")
  Member "cappronet deep feature learning via orthogonal projections onto capsule subspaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "subspace capsule network")
  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 "deep reinforcement learning using capsules in advanced game environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "examining the benefits of capsule neural networks")
  Member "sparse unsupervised capsules generalize better":
    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 "dropmax adaptive variational softmax":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "towards scalable efficient and accurate deep spiking neural networks with backward residual connections stochastic softmax and hybridization")

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)