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 "why my photos look sideways or upside down detecting canonical orientation of images using convolutional neural networks"):
  Abstract: Image orientation detection requires high-level scene understanding. Humans use object recognition and contextual scene information to correctly orient images. In literature, the problem of image orientation detection is mostly confronted by using low-level vision features, while some approaches incorporate few easily detectable semantic cues to gain minor improvements. The vast amount of semantic content in images makes orientation detection challenging, and therefore there is a large semantic gap between existing methods and human behavior. Also, existing methods in literature report highly discrepant detection rates, which is mainly due to large differences in datasets and limited variety of test images used for evaluation. In this work, for the first time, we leverage the power of deep learning and adapt pre-trained convolutional neural networks using largest training dataset to-date for the image orientation detection task. An extensive evaluation of our model on different public datasets shows that it remarkably generalizes to correctly orient a large set of unconstrained images; it also significantly outperforms the state-of-the-art and achieves accuracy very close to that of humans.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "places an image database for deep scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural architecture search for deep image prior")
  Member "compensating for large in plane rotations in natural images":
    also connected through other hyperedges:
      (none)
  Member "spatial transformer networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "a practical guide to cnns and fisher vectors for image instance retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a comprehensive analysis of deep regression")
  Member "how transferable are features in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "object detectors emerge in deep scene cnns":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")
  Member "imagenet large scale visual recognition challenge":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)