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: "attention mechanism in speaker recognition what does it learn in deep speaker embedding"
  Abstract: This paper presents an experimental study on deep speaker embedding with an attention mechanism that has been found to be a powerful representation learning technique in speaker recognition. In this framework, an attention model works as a frame selector that computes an attention weight for each frame-level feature vector, in accord with which an utterancelevel representation is produced at the pooling layer in a speaker embedding network. In general, an attention model is trained together with the speaker embedding network on a single objective function, and thus those two components are tightly bound to one another. In this paper, we consider the possibility that the attention model might be decoupled from its parent network and assist other speaker embedding networks and even conventional i-vector extractors. This possibility is demonstrated through a series of experiments on a NIST Speaker Recognition Evaluation (SRE) task, with 9.0% EER reduction and 3.8% min_Cprimary reduction when the attention weights are applied to i-vector extraction. Another experiment shows that DNN-based soft voice activity detection (VAD) can be effectively combined with the attention mechanism to yield further reduction of minCprimary by 6.6% and 1.6% in deep speaker embedding and i-vector systems, respectively.

Connected through hyperedge HE-1 (induced by source paper "robust speaker recognition using speech enhancement and attention model"):
  with members:
    - "audio visual speech enhancement using conditional variational auto encoder"
    - "self multi head attention for speaker recognition"
    - "speech enhancement with variational autoencoders and alpha stable distributions"
    - "attention based fully convolutional network for speech emotion recognition"
    - "cbam convolutional block attention module"
    - "voxceleb a large scale speaker identification dataset"
    - "squeeze and excitation networks"
    - "long short term memory networks for machine reading"

Connected through hyperedge HE-2 (induced by source paper "four principles of explainable ai as applied to biometrics and facial forensic algorithms"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "characterizing the variability in face recognition accuracy relative to race"
    - "generating visual explanations"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-3 (induced by source paper "h vectors utterance level speaker embedding using a hierarchical attention model"):
  with members:
    - "on deep speaker embeddings for text independent speaker recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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)