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 "pr epsilon epsilon ch a system for privacy preserving speech transcription"):
  Abstract: New Advances in machine learning and the abundance of speech datasets have made Automated Speech Recognition (ASR) systems, with very high accuracy, a reality. ASR systems offer their users the means to transcribe speech data at scale. Unfortunately, these systems pose serious privacy threats as speech is a rich source of sensitive acoustic and textual information. Although offline ASR eliminates the privacy risks, we find that its transcription performance is inferior to that of cloud-based ASR systems, especially for real-world recordings. In this paper, we propose Pr$\epsilon\epsilon$ch, an end-to-end speech transcription system which lies at an intermediate point in the privacy-utility spectrum of speech transcription. It protects the acoustic features of the speakers' voices and protects the privacy of the textual content at an improved performance relative to offline ASR. Pr$\epsilon\epsilon$ch relies on cloud-based services to transcribe a speech file after applying a series of privacy-preserving operations on the user's side. We perform a comprehensive evaluation of Pr$\epsilon\epsilon$ch, using diverse real-world datasets, that demonstrates its effectiveness. Pr$\epsilon\epsilon$ch provides transcriptions at a 12.30% to 32.24% improvement in word error rate over Deep Speech, while fully obfuscating the speakers' voice biometrics and allowing only a differentially private view of the textual content.

Includes members:
  Member "privacy preserving adversarial representation learning in asr reality or illusion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spreech a system for privacy preserving speech transcription")
  Member "the audio auditor participant level membership inference in internet of things voice services":
    also connected through other hyperedges:
      (none)
  Member "emotionless privacy preserving speech analysis for voice assistants":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spreech a system for privacy preserving speech transcription")
  Member "shrinkwrap differentially private query processing in private data federations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "crypt epsilon crypto assisted differential privacy on untrusted servers")
  Member "attacking speaker recognition with deep generative models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hear no evil see kenansville efficient and transferable black box attacks on speech recognition and voice identification systems")
  Member "voicemask anonymize and sanitize voice input on mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spreech a system for privacy preserving speech transcription")
  Member "tacotron towards end to end speech synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semi supervised neural architecture search")
  Member "wavenet a generative model for raw audio":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")

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)