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: "bilinear faster rcnn with ela for image tampering detection"
  Abstract: With technological advances leading to an increase in mechanisms of image tampering, our fraud detection methods must continue to be upgraded to match their sophistication. One problem with current methods is that they require prior knowledge of the method of forgery in order to determine which features to extract from the image to localize the region of interest. When a machine learning algorithm is used to learn different types tampering from a large set of various image types, with a big enough database we can easily classify which images are tampered (by training on the entire image feature map for each image), but we still are left with the question of which features to train on, and how to localize the manipulation. To solve this, object detection networks such as Faster RCNN, which combine an RPN (Region Proposal Network) with a CNN have recently been adapted to fraud detection by utilizing their ability to propose bounding boxes for objects of interest to localize the tampering artifacts. In this work, an existing bilinear Faster RCNN model that was developed will be modified with the second stream having an input of the ELA (Error Level Analysis) JPEG compression level mask.

Connected through hyperedge HE-1 (induced by source paper "multi stream faster rcnn for mitosis counting in breast cancer images"):
  with members:
    - "learning rich features for image manipulation detection"
    - "mitos rcnn a novel approach to mitotic figure detection in breast cancer histopathology images using region based convolutional neural networks"
    - "machine learning methods for histopathological image analysis"
    - "cell detection with deep convolutional neural network and compressed sensing"
    - "u net convolutional networks for biomedical image segmentation"
    - "compact bilinear pooling"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"

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)