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 "partial auc optimization based deep speaker embeddings with class center learning for text independent speaker verification"):
  Abstract: Deep embedding based text-independent speaker verification has demonstrated superior performance to traditional methods in many challenging scenarios. Its loss functions can be generally categorized into two classes, i.e., verification and identification. The verification loss functions match the pipeline of speaker verification, but their implementations are difficult. Thus, most state-of-the-art deep embedding methods use the identification loss functions with softmax output units or their variants. In this paper, we propose a verification loss function, named the maximization of partial area under the Receiver-operating-characteristic (ROC) curve (pAUC), for deep embedding based text-independent speaker verification. We also propose a class-center based training trial construction method to improve the training efficiency, which is critical for the proposed loss function to be comparable to the identification loss in performance. Experiments on the Speaker in the Wild (SITW) and NIST SRE 2016 datasets show that the proposed pAUC loss function is highly competitive with the state-of-the-art identification loss functions.

Includes members:
  Member "large margin softmax loss for speaker verification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a study on angular based embedding learning for text independent speaker verification")
  Member "deep speaker embedding learning with multi level pooling for text independent speaker verification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dropclass and dropadapt dropping classes for deep speaker representation learning")
  Member "optimization of the area under the roc curve using neural network supervectors for text dependent speaker verification":
    also connected through other hyperedges:
      (none)
  Member "voxceleb2 deep speaker recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "everybody s talkin let me talk as you want")
  Member "voxceleb a large scale speaker identification dataset":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "first order motion model for image animation")
  Member "deep speaker an end to end neural speaker embedding system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an end to end approach for the verification problem learning the right distance")
  Member "deep speaker feature learning for text independent speaker verification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vae based regularization for deep speaker embedding")
  Member "end to end text dependent speaker verification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dropclass and dropadapt dropping classes for deep speaker representation 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.LG(Machine Learning)