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: "speex a free codec for free speech"
  Abstract: The Speex project has been started in 2002 to address the need for a free, open-source speech codec. Speex is based on the Code Excited Linear Prediction (CELP) algorithm and, unlike the previously existing Vorbis codec, is optimised for transmitting speech for low latency communication over an unreliable packet network. This paper presents an overview of Speex, the technology involved in it and how it can be used in applications. The most recent developments in Speex, such as the fixed-point port, acoustic echo cancellation and noise suppression are also addressed.

Connected through hyperedge HE-1 (induced by source paper "low bit rate speech coding with vq vae and a wavenet decoder"):
  with members:
    - "transfer learning from speaker verification to multispeaker text to speech synthesis"
    - "sample efficient adaptive text to speech"
    - "end to end optimized speech coding with deep neural networks"
    - "neural discrete representation learning"
    - "end to end optimized image compression"
    - "composition of deep and spiking neural networks for very low bit rate speech coding"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-2 (induced by source paper "isolated and ensemble audio preprocessing methods for detecting adversarial examples against automatic speech recognition"):
  with members:
    - "did you hear that adversarial examples against automatic speech recognition"
    - "deflecting adversarial attacks with pixel deflection"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial example defenses ensembles of weak defenses are not strong"
    - "assessing threat of adversarial examples on deep neural networks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (induced by source paper "influence of speech codecs selection on transcoding steganography"):
  with members:
    - "padsteg introducing inter protocol steganography"
    - "using transcoding for hidden communication in ip telephony"
    - "steganography of voip streams"
    - "lack a voip steganographic method"
    - "new security and control protocol for voip based on steganography and digital watermarking"

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.SD(Sound)