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: "when provably secure steganography meets generative models"
  Abstract: Steganography is the art and science of hiding secret messages in public communication so that the presence of the secret messages cannot be detected. There are two provably secure steganographic frameworks, one is black-box sampling based and the other is compression based. The former requires a perfect sampler which yields data following the same distribution, and the latter needs explicit distributions of generative objects. However, these two conditions are too strict even unrealistic in the traditional data environment, because it is hard to model the explicit distribution of natural image. With the development of deep learning, generative models bring new vitality to provably secure steganography, which can serve as the black-box sampler or provide the explicit distribution of generative media. Motivated by this, this paper proposes two types of provably secure stegosystems with generative models. Specifically, we first design block-box sampling based provably secure stegosystem for broad generative models without explicit distribution, such as GAN, VAE, and flow-based generative models, where the generative network can serve as the perfect sampler. For compression based stegosystem, we leverage the generative models with explicit distribution such as autoregressive models instead, where the adaptive arithmetic coding plays the role of the perfect compressor, decompressing the encrypted message bits into generative media, and the receiver can compress the generative media into the encrypted message bits. To show the effectiveness of our method, we take DFC-VAE, Glow, WaveNet as instances of generative models and demonstrate the perfectly secure performance of these stegosystems with the state-of-the-art steganalysis methods.

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.MM(Multimedia)