human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "universal stego post processing for enhancing image steganography"):
  Abstract: It is well known that the modern steganography methods are designed under the framework of distortion minimization, and thus the design of embedding cost is a key issue. Instead of designing embedding cost in existing works, we propose a novel framework to enhance the steganography security via stego post-processing. To ensure the correct extraction of hidden message, we firstly analyze the characteristics of STCs (Syndrome-Trellis Codes) that are widely used in current steganography methods, and then design the rule for post-modification. Furthermore, since the steganography artifacts are typically reflected on image residuals, the proposed post-processing aims to reduce the residual distance between cover and the resulting stego. To this end, we model the proposed stego post-processing as a non-linear integer programming, and implement it via heuristic search. In addition, we carefully determine several important settings in our algorithm, such as the candidate embedding units to be dealt with, the direction and amplitude of post-modification, the adaptive filters for getting residuals, and the distance measure of residuals. Extensive experimental results evaluated on both hand-crafted steganalytic features and deep learning based ones demonstrate that the proposed method can enhance the security performance of various steganography methods effectively and efficiently. In addition, the proposed framework is universal because it can be applied to steganography both in spatial and JPEG domains.

Includes members:
  Member "cnn based adversarial embedding for image steganography":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recent advances of image steganography with generative adversarial networks")
  Member "deep convolutional neural network to detect j uniward":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning in steganography and steganalysis from 2015 to 2018")
  Member "intriguing properties of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial ranking attack and defense")

Question: Please tell me which class the source paper 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)