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: "automatic environmental sound recognition performance versus computational cost"
  Abstract: In the context of the Internet of Things, sound sensing applications are required to run on embedded platforms where notions of product pricing and form factor impose hard constraints on the available computing power. Whereas Automatic Environmental Sound Recognition AESR algorithms are most often developed with limited consideration for computational cost, this paper seeks which AESR algorithm can make the most of a limited amount of computing power by comparing the sound classification performance as a function of its computational cost. Results suggest that Deep Neural Networks yield the best ratio of sound classification accuracy across a range of computational costs, while Gaussian Mixture Models offer a reasonable accuracy at a consistently small cost, and Support Vector Machines stand between both in terms of compromise between accuracy and computational cost.

Connected through hyperedge HE-1 (induced by source paper "weakly labelled audioset tagging with attention neural networks"):
  with members:
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "attention based deep multiple instance learning"
    - "identify locate and separate audio visual object extraction in large video collections using weak supervision"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "audio set classification with attention model a probabilistic perspective"
    - "a structured self attentive sentence embedding"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "a joint detection classification model for audio tagging of weakly labelled data"

Connected through hyperedge HE-2 (induced by source paper "end to end environmental sound classification using a 1d convolutional neural network"):
  with members:
    - "randomly weighted cnns for music audio classification"
    - "end to end speech recognition from the raw waveform"
    - "sample level cnn architectures for music auto tagging using raw waveforms"
    - "learning from between class examples for deep sound recognition"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "very deep convolutional neural networks for raw waveforms"
    - "cnn architectures for large scale audio classification"
    - "soundnet learning sound representations from unlabeled video"

Connected through hyperedge HE-3 (induced by source paper "user environment detection with acoustic sensors embedded on mobile devices for the recognition of activities of daily living"):
  with members:
    - "data fusion on motion and magnetic sensors embedded on mobile devices for the identification of activities of daily living"
    - "pattern recognition techniques for the identification of activities of daily living using mobile device accelerometer"
    - "sensingkit evaluating the sensor power consumption in ios devices"
    - "bigear inferring the ambient and emotional correlates from smartphone based acoustic big data"
    - "mobile device identification via sensor fingerprinting"

Connected through hyperedge HE-4 (induced by source paper "empirical study of drone sound detection in real life environment with deep neural networks"):
  with members:
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"
    - "acoustic scene classification classifying environments from the sounds they produce"

Connected through hyperedge HE-5 (induced by source paper "deep convolutional neural networks and data augmentation for environmental sound classification"):
  with members:
    - "the implementation of low cost urban acoustic monitoring devices"
    - "recurrent neural networks for polyphonic sound event detection in real life recordings"

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)