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: "learning how to listen a temporal frequential attention model for sound event detection"
  Abstract: In this paper, we propose a temporal-frequential attention model for sound event detection (SED). Our network learns how to listen with two attention models: a temporal attention model and a frequential attention model. Proposed system learns when to listen using the temporal attention model while it learns where to listen on the frequency axis using the frequential attention model. With these two models, we attempt to make our system pay more attention to important frames or segments and important frequency components for sound event detection. Our proposed method is demonstrated on the task 2 of Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 Challenge and achieves competitive performance.

Connected through hyperedge HE-1 (induced by source paper "multi scale time frequency attention for acoustic event detection"):
  with members:
    - "r crnn region based convolutional recurrent neural network for audio event detection"
    - "conversational analysis using utterance level attention based bidirectional recurrent neural networks"
    - "a simple model for detection of rare sound events"
    - "residual attention network for image classification"
    - "stacked hourglass networks for human pose estimation"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "multi scale time frequency attention for rare sound event detection"):
  with members:
    - "r crnn region based convolutional recurrent neural network for audio event detection"
    - "conversational analysis using utterance level attention based bidirectional recurrent neural networks"
    - "a simple model for detection of rare sound events"
    - "stacked hourglass networks for human pose estimation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)