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 "cnn based steganalysis and parametric adversarial embedding a game theoretic framework"):
  Abstract: CNN-based steganalysis has recently achieved very good performance in detecting content-adaptive steganography. At the same time, recent works have shown that, by adopting an approach similar to that used to build adversarial examples, a steganographer can adopt an adversarial embedding strategy to effectively counter a target CNN steganalyzer. In turn, the good performance of the steganalyzer can be restored by retraining the CNN with adversarial stego images. A problem with this model is that, arguably, at training time the steganalizer is not aware of the exact parameters used by the steganograher for adversarial embedding and, vice versa, the steganographer does not know how the images that will be used to train the steganalyzer are generated. In order to exit this apparent deadlock, we introduce a game theoretic framework wherein the problem of setting the parameters of the steganalyzer and the steganographer is solved in a strategic way. More specifically, a non-zero sum game is first formulated to model the problem, and then instantiated by considering a specific adversarial embedding scheme setting its operating parameters in a game-theoretic fashion. Our analysis shows that the equilibrium solution of the non zero-sum game can be conveniently found by solving an associated zero-sum game, thus reducing greatly the complexity of the problem. Then we run several experiments to derive the optimum strategies for the steganographer and the staganalyst in a game-theoretic sense, and to evaluate the performance of the game at the equilibrium, characterizing the loss with respect to the conventional non-adversarial case. Eventually, by leveraging on the analysis of the equilibrium point of the game, we introduce a new strategy to improve the reliability of the steganalysis, which shows the benefits of addressing the security issue in a game-theoretic perspective.

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 "adversarial source identification game with corrupted training":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "detection games under fully active adversaries")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "a game theoretic framework for optimum decision fusion in the presence of byzantines":
    also connected through other hyperedges:
      (none)
  Member "binary hypothesis testing game with training data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cnn based adversarial embedding for image steganography")
  Member "intriguing properties of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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)