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 "calibrated steganalysis of mp3stego in multi encoder scenario"):
  Abstract: Comparing popularity of mp3 and wave with the amount of works published on each of them shows mp3 steganalysis has not found adequate attention. Furthermore, investigating existing works on mp3 steganalysis shows that a major factor has been overlooked. Experimenting with different mp3 encoders shows there are subtle differences in their outputs. This shows that mp3 standard has been implemented in dissimilar fashions, which in turn could degrade performance of steganalysis if it is not addressed properly. Additionally, calibration is a powerful technique which has not found its true potential for mp3 steganalysis. This paper tries to fill these gaps. First, we present our analysis on different encoders and show they can be classified quite accurately with only four features. Then, we propose a new set of calibrated features based on quantization step. To that end, we show quantization step is a band limited signal and steganography noise affects its high frequency components more prominently. By applying a low pass filter on quantization steps, we arrive at an estimation of quantization step, which in turn is used for calibrating the features.

Includes members:
  Member "decomposition based transfer distance metric learning for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transfer learning with dynamic distribution adaptation")
  Member "multi layer architecture for efficient steganalysis of undermp3cover in multi encoder scenario":
    also connected through other hyperedges:
      (none)
  Member "comprehensive review of audio steganalysis methods":
    also connected through other hyperedges:
      (none)
  Member "universal audio steganalysis based on calibration and reversed frequency resolution of human auditory system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi layer architecture for efficient steganalysis of undermp3cover in multi encoder scenario")
  Member "infinite latent feature selection a probabilistic latent graph based ranking approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a matlab toolbox for feature importance ranking")
  Member "unsupervised feature selection with adaptive structure learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi participant multi class vertical federated learning")
  Member "generalized fisher score for feature selection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")

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.CR(Cryptography and Security)