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: "evaluating the effectiveness of automated identity masking aim methods with human perception"
  Abstract: Face de-identification algorithms have been developed in response to the prevalent use of public video recordings and surveillance cameras. Here, we evaluated the success of identity masking in the context of monitoring drivers as they actively operate a motor vehicle. We compared the effectiveness of eight de-identification algorithms using human perceivers. The algorithms we tested included the personalized supervised bilinear regression method for Facial Action Transfer (FAT), the DMask method, which renders a generic avatar face, and two edge-detection methods implemented with and without image polarity inversion (Canny, Scharr). We also used an Overmask approach that combined the FAT and Canny methods. We compared these identity masking methods to identification of an unmasked video of the driver. Human subjects were tested in a standard face recognition experiment in which they learned driver identities with a high resolution (studio-style) image, and were tested subsequently on their ability to recognize masked and unmasked videos of these individuals driving. All masking methods lowered identification accuracy substantially, relative to the unmasked video. The most successful methods, DMask and Canny, lowered human identification performance to near random. In all cases, identifications were made with stringent decision criteria indicating the subjects had low confidence in their decisions. We conclude that carefully tested de-identification approaches, used alone or in combination, can be an effective tool for protecting the privacy of individuals captured in videos. Future work should examine how the most effective methods fare in preserving facial action 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)