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 "deep nets what have they ever done for vision"):
  Abstract: This is an opinion paper about the strengths and weaknesses of Deep Nets. They are at the center of recent progress on Artificial Intelligence and are of growing importance in Cognitive Science and Neuroscience since they enable the development of computational models that can deal with a large range of visually realistic stimuli and visual tasks. They have clear limitations but they also have enormous successes. There is also gradual, though incomplete, understanding of their inner workings. It seems unlikely that Deep Nets in their current form will be the best long-term solution either for building general purpose intelligent machines or for understanding the mind/brain, but it is likely that many aspects of them will remain. At present Deep Nets do very well on specific types of visual tasks and on specific benchmarked datasets. But Deep Nets are much less general purpose, flexible, and adaptive than the human visual system. Moreover, methods like Deep Nets may run into fundamental difficulties when faced with the enormous complexity of natural images. To illustrate our main points, while keeping the references small, this paper is slightly biased towards work from our group.

Includes members:
  Member "clevr ref diagnosing visual reasoning with referring expressions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cops ref a new dataset and task on compositional referring expression comprehension")
  Member "the elephant in the room":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "deep learning a critical appraisal":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "strike with a pose neural networks are easily fooled by strange poses of familiar objects":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial ranking attack and defense")
  Member "progressive neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "few shot image recognition by predicting parameters from activations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting meta learning as supervised learning")
  Member "unsupervised learning of depth and ego motion from video":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")

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)