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 "robust acoustic scene classification using a multi spectrogram encoder decoder framework"):
  Abstract: This article proposes an encoder-decoder network model for Acoustic Scene Classification (ASC), the task of identifying the scene of an audio recording from its acoustic signature. We make use of multiple low-level spectrogram features at the front-end, transformed into higher level features through a well-trained CNN-DNN front-end encoder. The high level features and their combination (via a trained feature combiner) are then fed into different decoder models comprising random forest regression, DNNs and a mixture of experts, for back-end classification. We report extensive experiments to evaluate the accuracy of this framework for various ASC datasets, including LITIS Rouen and IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 Task 1, 2017 Task 1, 2018 Tasks 1A & 1B and 2019 Tasks 1A & 1B. The experimental results highlight two main contributions; the first is an effective method for high-level feature extraction from multi-spectrogram input via the novel C-DNN architecture encoder network, and the second is the proposed decoder which enables the framework to achieve competitive results on various datasets. The fact that a single framework is highly competitive for several different challenges is an indicator of its robustness for performing general ASC tasks.

Includes members:
  Member "enhancing sound texture in cnn based acoustic scene classification":
    also connected through other hyperedges:
      (none)
  Member "acoustic scene classification by implicitly identifying distinct sound events":
    also connected through other hyperedges:
      (none)
  Member "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "source separation with weakly labelled data an approach to computational auditory scene analysis")
  Member "spatio temporal attention pooling for audio scene classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning discriminative and robust time frequency representations for environmental sound classification")
  Member "enabling early audio event detection with neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unifying isolated and overlapping audio event detection with multi label multi task convolutional recurrent neural networks")
  Member "a compact and discriminative feature based on auditory summary statistics for acoustic scene classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "acoustic scene classification by implicitly identifying distinct sound events")
  Member "dcase 2018 challenge surrey cross task convolutional neural network baseline":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cure dataset ladder networks for audio event classification")
  Member "beyond equal length snippets how long is sufficient to recognize an audio scene":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spatio temporal attention pooling for audio scene classification")

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)