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 "adversarial speaker verification"):
  Abstract: The use of deep networks to extract embeddings for speaker recognition has proven successfully. However, such embeddings are susceptible to performance degradation due to the mismatches among the training, enrollment, and test conditions. In this work, we propose an adversarial speaker verification (ASV) scheme to learn the condition-invariant deep embedding via adversarial multi-task training. In ASV, a speaker classification network and a condition identification network are jointly optimized to minimize the speaker classification loss and simultaneously mini-maximize the condition loss. The target labels of the condition network can be categorical (environment types) and continuous (SNR values). We further propose multi-factorial ASV to simultaneously suppress multiple factors that constitute the condition variability. Evaluated on a Microsoft Cortana text-dependent speaker verification task, the ASV achieves 8.8% and 14.5% relative improvements in equal error rates (EER) for known and unknown conditions, respectively.

Includes members:
  Member "adversarial speaker adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attentive adversarial learning for domain invariant training")
  Member "the fifth chime speech separation and recognition challenge dataset task and baselines":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on loss functions for supervised monaural time domain speech enhancement")
  Member "unsupervised adaptation with domain separation networks for robust speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self supervised speech recognition via local prior matching")
  Member "english conversational telephone speech recognition by humans and machines":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed training of deep neural network acoustic models for automatic speech recognition")
  Member "segan speech enhancement generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "when relation networks meet gans relation gans with triplet loss")
  Member "deep speaker an end to end neural speaker embedding system":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an end to end approach for the verification problem learning the right distance")
  Member "exploring speech enhancement with generative adversarial networks for robust speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improving gans for speech enhancement")
  Member "end to end attention based text dependent speaker verification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "biometric recognition using deep learning a survey")

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.LG(Machine Learning)