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: "privacy preserving adversarial representation learning in asr reality or illusion"
  Abstract: Automatic speech recognition (ASR) is a key technology in many services and applications. This typically requires user devices to send their speech data to the cloud for ASR decoding. As the speech signal carries a lot of information about the speaker, this raises serious privacy concerns. As a solution, an encoder may reside on each user device which performs local computations to anonymize the representation. In this paper, we focus on the protection of speaker identity and study the extent to which users can be recognized based on the encoded representation of their speech as obtained by a deep encoder-decoder architecture trained for ASR. Through speaker identification and verification experiments on the Librispeech corpus with open and closed sets of speakers, we show that the representations obtained from a standard architecture still carry a lot of information about speaker identity. We then propose to use adversarial training to learn representations that perform well in ASR while hiding speaker identity. Our results demonstrate that adversarial training dramatically reduces the closed-set classification accuracy, but this does not translate into increased open-set verification error hence into increased protection of the speaker identity in practice. We suggest several possible reasons behind this negative result.

Connected through hyperedge HE-1 (induced by source paper "pr epsilon epsilon ch a system for privacy preserving speech transcription"):
  with members:
    - "the audio auditor participant level membership inference in internet of things voice services"
    - "emotionless privacy preserving speech analysis for voice assistants"
    - "shrinkwrap differentially private query processing in private data federations"
    - "attacking speaker recognition with deep generative models"
    - "voicemask anonymize and sanitize voice input on mobile devices"
    - "tacotron towards end to end speech synthesis"
    - "wavenet a generative model for raw audio"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-2 (induced by source paper "spreech a system for privacy preserving speech transcription"):
  with members:
    - "emotionless privacy preserving speech analysis for voice assistants"
    - "shrinkwrap differentially private query processing in private data federations"
    - "attacking speaker recognition with deep generative models"
    - "voicemask anonymize and sanitize voice input on mobile devices"
    - "deep speech scaling up end to end speech recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "evaluating voice conversion based privacy protection against informed attackers"):
  with members:
    - "one shot voice conversion by separating speaker and content representations with instance normalization"
    - "stargan vc non parallel many to many voice conversion with star generative adversarial networks"
    - "espnet end to end speech processing toolkit"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"

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.CL(Computation and Language)