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: "basic level categorization facilitates visual object recognition"
  Abstract: Recent advances in deep learning have led to significant progress in the computer vision field, especially for visual object recognition tasks. The features useful for object classification are learned by feed-forward deep convolutional neural networks (CNNs) automatically, and they are shown to be able to predict and decode neural representations in the ventral visual pathway of humans and monkeys. However, despite the huge amount of work on optimizing CNNs, there has not been much research focused on linking CNNs with guiding principles from the human visual cortex. In this work, we propose a network optimization strategy inspired by both of the developmental trajectory of children's visual object recognition capabilities, and Bar (2003), who hypothesized that basic level information is carried in the fast magnocellular pathway through the prefrontal cortex (PFC) and then projected back to inferior temporal cortex (IT), where subordinate level categorization is achieved. We instantiate this idea by training a deep CNN to perform basic level object categorization first, and then train it on subordinate level categorization. We apply this idea to training AlexNet (Krizhevsky et al., 2012) on the ILSVRC 2012 dataset and show that the top-5 accuracy increases from 80.13% to 82.14%, demonstrating the effectiveness of the method. We also show that subsequent transfer learning on smaller datasets gives superior results.

Connected through hyperedge HE-1 (induced by source paper "curriculum learning by transfer learning theory and experiments with deep networks"):
  with members:
    - "automated curriculum learning for neural networks"
    - "cased curriculum adaptive sampling for extreme data imbalance"
    - "training region based object detectors with online hard example mining"
    - "learning structured inference neural networks with label relations"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "compressing neural networks with the hashing trick"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-2 (induced by source paper "theory of curriculum learning with convex loss functions"):
  with members:
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "training region based object detectors with online hard example mining"
    - "facenet a unified embedding for face recognition and clustering"

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.CV(Computer Vision and Pattern Recognition)