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 "towards an interactive and interpretable cad system to support proximal femur fracture classification"):
  Abstract: Fractures of the proximal femur represent a critical entity in the western world, particularly with the growing elderly population. Such fractures result in high morbidity and mortality, reflecting a significant health and economic impact on our society. Different treatment strategies are recommended for different fracture types, with surgical treatment still being the gold standard in most of the cases. The success of the treatment and prognosis after surgery strongly depends on an accurate classification of the fracture among standard types, such as those defined by the AO system. However, the classification of fracture types based on x-ray images is difficult as confirmed by low intra- and inter-expert agreement rates of our in-house study and also in the previous literature. The presented work proposes a fully automatic computer-aided diagnosis (CAD) tool, based on current deep learning techniques, able to identify, localize and finally classify proximal femur fractures on x-rays images according to the AO classification. Results of our experimental evaluation show that the performance achieved by the proposed CAD tool is comparable to the average expert for the classification of x-ray images into types ''A'', ''B'' and ''normal'' (precision of 89%), while the performance is even superior when classifying fractures versus ''normal'' cases (precision of 94%). In addition, the integration of the proposed CAD tool into daily clinical routine is extensively discussed, towards improving the interface between humans and AI-powered machines in supporting medical decisions.

Includes members:
  Member "weakly supervised localization and classification of proximal femur fractures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly supervised universal fracture detection in pelvic x rays")
  Member "deep convolutional networks for automated detection of posterior element fractures on spine ct":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "feature learning to automatically assess radiographic knee osteoarthritis severity")
  Member "understanding anatomy classification through attentive response maps":
    also connected through other hyperedges:
      (none)
  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 "visualizing and understanding convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)