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: "adversarial network bottleneck features for noise robust speaker verification"
  Abstract: In this paper, we propose a noise robust bottleneck feature representation which is generated by an adversarial network (AN). The AN includes two cascade connected networks, an encoding network (EN) and a discriminative network (DN). Mel-frequency cepstral coefficients (MFCCs) of clean and noisy speech are used as input to the EN and the output of the EN is used as the noise robust feature. The EN and DN are trained in turn, namely, when training the DN, noise types are selected as the training labels and when training the EN, all labels are set as the same, i.e., the clean speech label, which aims to make the AN features invariant to noise and thus achieve noise robustness. We evaluate the performance of the proposed feature on a Gaussian Mixture Model-Universal Background Model based speaker verification system, and make comparison to MFCC features of speech enhanced by short-time spectral amplitude minimum mean square error (STSA-MMSE) and deep neural network-based speech enhancement (DNN-SE) methods. Experimental results on the RSR2015 database show that the proposed AN bottleneck feature (AN-BN) dramatically outperforms the STSA-MMSE and DNN-SE based MFCCs for different noise types and signal-to-noise ratios. Furthermore, the AN-BN feature is able to improve the speaker verification performance under the clean condition.

Connected through hyperedge HE-1 (induced by source paper "time contrastive learning based deep bottleneck features for text dependent speaker verification"):
  with members:
    - "end to end neural segmental models for speech recognition"
    - "end to end attention based text dependent speaker verification"
    - "sequence prediction with neural segmental models"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "end to end training approaches for discriminative segmental models"
    - "understanding deep learning requires rethinking generalization"
    - "incorporating pass phrase dependent background models for text dependent speaker verification"
    - "unsupervised feature learning from temporal data"

Connected through hyperedge HE-2 (induced by source paper "mobile big data analysis with machine learning"):
  with members:
    - "decorrelation of neutral vector variables theory and applications"
    - "distributed optimization using the primal dual method of multipliers"
    - "cross modal subspace learning for fine grained sketch based image retrieval"
    - "the role of data analysis in the development of intelligent energy networks"
    - "big data caching for networking moving from cloud to edge"
    - "mobile big data analytics using deep learning and apache spark"
    - "soft defined heterogeneous vehicular network architecture and challenges"
    - "5g wireless backhaul networks challenges and research advances"

Connected through hyperedge HE-3 (induced by source paper "training multi task adversarial network for extracting noise robust speaker embedding"):
  with members:
    - "mtgan speaker verification through multitasking triplet generative adversarial networks"
    - "deep speaker an end to end neural speaker embedding system"
    - "aishell 1 an open source mandarin speech corpus and a speech recognition baseline"
    - "end to end text dependent speaker verification"

Connected through hyperedge HE-4 (induced by source paper "cosine similarity based adversarial process"):
  with members:
    - "adversarial discriminative domain adaptation"
    - "coupled generative adversarial networks"
    - "a lightened cnn for deep face representation"
    - "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)