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 "joint deep exploitation of semantic keywords and visual features for malicious crowd image classification"):
  Abstract: General image classification approaches differentiate classes using strong distinguishing features but some classes cannot be easily separated because they contain very similar visual features. To deal with this problem, we can use keywords relevant to a particular class. To implement this concept we have newly constructed a malicious crowd dataset which contains crowd images with two events, benign and malicious, which look similar yet involve opposite semantic events. We also created a set of five malicious event-relevant keywords such as police and fire. In the evaluation, integrating malicious event classification with recognition output of these keywords enhances the overall performance on the malicious crowd dataset.

Includes members:
  Member "iod cnn integrating object detection networks for event recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "s dod cnn doubly injecting spatially preserved object information for event recognition")
  Member "weakly supervised localization using deep feature maps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dual attention guided dropblock module for weakly supervised object localization")
  Member "top down neural attention by excitation backprop":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "dynamic belief fusion for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "s dod cnn doubly injecting spatially preserved object information for event recognition")

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)