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 "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"):
  Abstract: Audio tagging aims to perform multi-label classification on audio chunks and it is a newly proposed task in the Detection and Classification of Acoustic Scenes and Events 2016 (DCASE 2016) challenge. This task encourages research efforts to better analyze and understand the content of the huge amounts of audio data on the web. The difficulty in audio tagging is that it only has a chunk-level label without a frame-level label. This paper presents a weakly supervised method to not only predict the tags but also indicate the temporal locations of the occurred acoustic events. The attention scheme is found to be effective in identifying the important frames while ignoring the unrelated frames. The proposed framework is a deep convolutional recurrent model with two auxiliary modules: an attention module and a localization module. The proposed algorithm was evaluated on the Task 4 of DCASE 2016 challenge. State-of-the-art performance was achieved on the evaluation set with equal error rate (EER) reduced from 0.13 to 0.11, compared with the convolutional recurrent baseline system.

Includes members:
  Member "convolutional gated recurrent neural network incorporating spatial features for audio tagging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recurrent neural networks an embedded computing perspective")
  Member "seed expand and constrain three principles for weakly supervised image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "audio event detection using weakly labeled data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "limitations of weak labels for embedding and tagging")
  Member "youtube 8m a large scale video classification benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "symbiotic attention with privileged information for egocentric action recognition")
  Member "fully dnn based multi label regression for audio tagging":
    also connected through other hyperedges:
      (none)
  Member "yfcc100m the new data in multimedia research":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "in defense of grid features for visual question answering")
  Member "end to end attention based large vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "attention based models for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient content based sparse attention with routing transformers")

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)