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 "multi scale time frequency attention for rare sound event detection"):
  Abstract: Attention mechanism has been widely applied to various sound-related tasks. In this work, we propose a Multi-Scale Time-Frequency Attention (MTFA) module for sound event detection. By generating an attention heatmap, MTFA enables the model to focus on discriminative components of the spectrogram along both time and frequency axis. Besides, gathering information at multiple scales helps the model adapt better to the characteristics of different categories of target events. The proposed method is demonstrated on task 2 of Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 Challenge. To the best of our knowledge, our method outperforms all previous methods that don't use model ensemble on development dataset and achieves state-of-the-art on evaluation dataset by reducing the error rate to 0.09 from 0.13. This demonstrates the effectiveness of MTFA on retrieving discriminative representations for sound event detection.

Includes members:
  Member "r crnn region based convolutional recurrent neural network for audio event detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi scale time frequency attention for acoustic event detection")
  Member "conversational analysis using utterance level attention based bidirectional recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "eda enriching emotional dialogue acts using an ensemble of neural annotators")
  Member "learning how to listen a temporal frequential attention model for sound event detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi scale time frequency attention for acoustic event detection")
  Member "a simple model for detection of rare sound events":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi scale time frequency attention for acoustic event detection")
  Member "stacked hourglass networks for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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.SD(Sound)