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 "alpha pooling for convolutional neural networks"):
  Abstract: Convolutional neural networks (CNNs) have achieved remarkable performance in many applications, especially image recognition. As a crucial component of CNNs, sub-sampling plays an important role, and max pooling and arithmetic average pooling are commonly used sub-sampling methods. In addition to the two pooling methods, however, there could be many other pooling types, such as geometric average, harmonic average, and so on. Since it is not easy for algorithms to find the best pooling method, human experts choose types of pooling, which might not be optimal for different tasks. Following deep learning philosophy, the type of pooling can be driven by data for a given task. In this paper, we propose {\em alpha-pooling}, which has a trainable parameter $\alpha$ to decide the type of pooling. Alpha-pooling is a general pooling method including max pooling and arithmetic average pooling as a special case, depending on the parameter $\alpha$. In experiments, alpha-pooling improves the accuracy of image recognition tasks, and we found that max pooling is not the optimal pooling scheme. Moreover each layer has different optimal pooling types.

Includes members:
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "compact bilinear pooling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "component analysis for visual question answering architectures")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "stochastic pooling for regularization of deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "end to end learning local multi view descriptors for 3d point clouds")

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.LG(Machine Learning)