human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "iod cnn integrating object detection networks for event recognition"):
  with members:
    - "object scene convolutional neural networks for event recognition in images"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "dynamic belief fusion for object detection"
    - "better exploiting os cnns for better event recognition in images"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "detection bank an object detection based video representation for multimedia event recognition"

Question: Please tell me which class the center node 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)