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 "voice spoofing detection corpus for single and multi order audio replays"):
  Abstract: The evolution of modern voice controlled devices (VCDs) in recent years has revolutionized the Internet of Things, and resulted in increased realization of smart homes, personalization and home automation through voice commands. The introduction of VCDs in IoT is expected to give emergence of new subfield of IoT, called Multimedia of Thing (MoT). These VCDs can be exploited in IoT driven environment to generate various spoofing attacks including the replays. Replay attacks are generated through replaying the recorded audio of legitimate human speaker with the intent of deceiving the VCDs having speaker verification system. The connectivity among the VCDs can easily be exploited in IoT driven environment to generate a chain of replay attacks (multi-order replay attacks). Existing spoofing detection datasets like ASVspoof and ReMASC contain only the first-order replay recordings against the bonafide audio samples. These datasets can not offer evaluation of the anti-spoofing algorithms capable of detecting the multi-order replay attacks. Additionally, these datasets do not capture the characteristics of microphone arrays, which is an important characteristic of modern VCDs. We need a diverse replay spoofing detection corpus that consists of multi-order replay recordings against the bonafide voice samples. This paper presents a novel voice spoofing detection corpus (VSDC) to evaluate the performance of multi-order replay anti-spoofing methods. The proposed VSDC consists of first and second-order-replay samples against the bonafide audio recordings. Additionally, the proposed VSDC can also be used to evaluate the performance of speaker verification systems as our corpus includes the audio samples of fifteen different speakers. To the best of our knowledge, this is the first publicly available replay spoofing detection corpus comprising of first-order and second-order-replay samples.

Includes members:
  Member "remasc realistic replay attack corpus for voice controlled systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "detecting replay attacks using multi channel audio a neural network based method")
  Member "stc anti spoofing systems for the asvspoof 2015 challenge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "introduction to voice presentation attack detection and recent advances")

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.SD(Sound)