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: "voices obscured in complex environmental settings voices corpus"
  Abstract: This paper introduces the Voices Obscured In Complex Environmental Settings (VOICES) corpus, a freely available dataset under Creative Commons BY 4.0. This dataset will promote speech and signal processing research of speech recorded by far-field microphones in noisy room conditions. Publicly available speech corpora are mostly composed of isolated speech at close-range microphony. A typical approach to better represent realistic scenarios, is to convolve clean speech with noise and simulated room response for model training. Despite these efforts, model performance degrades when tested against uncurated speech in natural conditions. For this corpus, audio was recorded in furnished rooms with background noise played in conjunction with foreground speech selected from the LibriSpeech corpus. Multiple sessions were recorded in each room to accommodate for all foreground speech-background noise combinations. Audio was recorded using twelve microphones placed throughout the room, resulting in 120 hours of audio per microphone. This work is a multi-organizational effort led by SRI International and Lab41 with the intent to push forward state-of-the-art distant microphone approaches in signal processing and speech recognition.

Connected through hyperedge HE-1 (induced by source paper "deep speaker embeddings for far field speaker recognition on short utterances"):
  with members:
    - "stc speaker recognition systems for the voices from a distance challenge"
    - "softmax dissection towards understanding intra and inter class objective for embedding learning"
    - "on deep speaker embeddings for text independent speaker recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "voxceleb2 deep speaker recognition"
    - "additive margin softmax for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "voxceleb a large scale speaker identification dataset"

Connected through hyperedge HE-2 (induced by source paper "reducing audio membership inference attack accuracy to chance 4 defenses"):
  with members:
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "audio adversarial examples targeted attacks on speech to text"
    - "attacking machine learning models as part of a cyber kill chain"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "membership inference attacks against machine learning models"
    - "distillation as a defense to adversarial perturbations against deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "stc speaker recognition systems for the voices from a distance challenge"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "a light cnn for deep face representation with noisy labels"

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)