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: "universal audio steganalysis based on calibration and reversed frequency resolution of human auditory system"
  Abstract: Calibration and higher order statistics (HOS) are standard components of many image steganalysis systems. These techniques have not yet found adequate attention in audio steganalysis context. Specifically, most of current works are either non-calibrated or only based on noise removal approach. This paper aims to fill these gaps by proposing a new set of calibrated features based on re-embedding technique. Additionally, we show that least significant bit (LSB) is the most sensitive bit-plane to data hiding algorithms and therefore it can be employed as a universal embedding method. Furthermore, the proposed features are based on a model that has the maximum deviation from human auditory system (HAS), and therefore are more suitable for the purpose of steganalysis. Performance of the proposed method is evaluated on a wide range of data hiding algorithms in both targeted and universal paradigms. Simulation results show that the proposed method can detect the finest traces of data hiding algorithms and in very low embedding rates. The system detects steghide at capacity of 0.06 bit per symbol (BPS) with sensitivity of 98.6% (music) and 78.5% (speech). These figures are respectively 7.1% and 27.5% higher than state-of-the-art results based on RMFCC.

Connected through hyperedge HE-1 (induced by source paper "multi layer architecture for efficient steganalysis of undermp3cover in multi encoder scenario"):
  with members:
    - "calibrated steganalysis of mp3stego in multi encoder scenario"

Connected through hyperedge HE-2 (induced by source paper "calibrated steganalysis of mp3stego in multi encoder scenario"):
  with members:
    - "decomposition based transfer distance metric learning for image classification"
    - "multi layer architecture for efficient steganalysis of undermp3cover in multi encoder scenario"
    - "comprehensive review of audio steganalysis methods"
    - "infinite latent feature selection a probabilistic latent graph based ranking approach"
    - "unsupervised feature selection with adaptive structure learning"
    - "generalized fisher score for feature selection"

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)