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 "leveraging deep neural networks to capture psychological representations"):
  Abstract: Artificial neural networks have seen a recent surge in popularity for their ability to solve complex problems as well as or better than humans. In computer vision, deep convolutional neural networks have become the standard for object classification and image understanding due to their ability to learn efficient representations of high-dimensional data. However, the relationship between these representations and human psychological representations has remained unclear. Here we evaluate the quantitative and qualitative nature of this correspondence. We find that state-of-the-art object classification networks provide a reasonable first approximation to human similarity judgments, but fail to capture some of the structure of psychological representations. We show that a simple transformation that corrects these discrepancies can be obtained through convex optimization. Such representations provide a tool that can be used to study human performance on complex tasks with naturalistic stimuli, such as predicting the difficulty of learning novel categories. Our results extend the scope of psychological experiments and computational modeling of cognition by enabling tractable use of large natural stimulus sets.

Includes members:
  Member "adapting deep network features to capture psychological representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "high level perceptual similarity is enabled by learning diverse tasks")
  Member "adversarial feature learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "natural language processing almost from scratch":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

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)