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: "reversible image watermarking for health informatics systems using distortion compensation in wavelet domain"
  Abstract: Reversible image watermarking guaranties restoration of both original cover and watermark logo from the watermarked image. Capacity and distortion of the image under reversible watermarking are two important parameters. In this study a reversible watermarking is investigated with focusing on increasing the embedding capacity and reducing the distortion in medical images. Integer wavelet transform is used for embedding where in each iteration, one watermark bit is embedded in one transform coefficient. We devise a novel approach that when a coefficient is modified in an iteration, the produced distortion is compensated in the next iteration. This distortion compensation method would result in low distortion rate. The proposed method is tested on four types of medical images including MRI of brain, cardiac MRI, MRI of breast, and intestinal polyp images. Using a one-level wavelet transform, maximum capacity of 1.5 BPP is obtained. Experimental results demonstrate that the proposed method is superior to the state-of-the-art works in terms of capacity and distortion.

Connected through hyperedge HE-1 (induced by source paper "blessmark a blind diagnostically lossless watermarking framework for medical applications based on deep neural networks"):
  with members:
    - "convolutional neural network architecture for recovering watermark synchronization"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "densely connected convolutional networks"
    - "tensorflow a system for large scale machine learning"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "mimosanet an unrobust neural network preventing model stealing"):
  with members:
    - "robust watermarking of neural network with exponential weighting"
    - "have you stolen my model evasion attacks against deep neural network watermarking techniques"
    - "deepobfuscation securing the structure of convolutional neural networks via knowledge distillation"
    - "adam a method for stochastic optimization"

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)