human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "curriculum by texture"
  Abstract: Convolutional Neural Networks (CNNs) have shown impressive performance in computer vision tasks such as image classification and segmentation. One factor for the success of CNNs is that they have an inductive bias that assumes a certain type of spatial structure is present in the data. Recent work by Geirhos et al. (2018) shows how learning in CNNs causes the learned CNN models to be biased towards high-frequency textural information, compared to low-frequency shape information in images. Many tasks generally requires both shape and textural information. Hence, we propose a simple curriculum based scheme which improves the ability of CNNs to be less biased towards textural information, and at the same time, being able to represent both the shape and textural information. We propose to augment the training of CNNs by controlling the amount of textural information that is available to the CNNs during the training process, by convolving the output of a CNN layer with a low-pass filter, or simply a Gaussian kernel. By reducing the standard deviation of the Gaussian kernel, we are able to gradually increase the amount of textural information available as training progresses, and hence reduce the texture bias. Such an augmented training scheme significantly improves the performance of CNNs on various image classification tasks, while adding no additional trainable parameters or auxiliary regularization objectives. We also observe significant improvements when using the trained CNNs to perform transfer learning on a different dataset, and transferring to a different task which shows how the learned CNNs using the proposed method act as better feature extractors.

Question: Please tell me which class the center node 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)