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: "experiments on the dcase challenge 2016 acoustic scene classification and sound event detection in real life recording"
  Abstract: In this paper we present our work on Task 1 Acoustic Scene Classification and Task 3 Sound Event Detection in Real Life Recordings. Among our experiments we have low-level and high-level features, classifier optimization and other heuristics specific to each task. Our performance for both tasks improved the baseline from DCASE: for Task 1 we achieved an overall accuracy of 78.9% compared to the baseline of 72.6% and for Task 3 we achieved a Segment-Based Error Rate of 0.48 compared to the baseline of 0.91.

Connected through hyperedge HE-1 (induced by source paper "robust acoustic scene classification using a multi spectrogram encoder decoder framework"):
  with members:
    - "enhancing sound texture in cnn based acoustic scene classification"
    - "acoustic scene classification by implicitly identifying distinct sound events"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "spatio temporal attention pooling for audio scene classification"
    - "enabling early audio event detection with neural networks"
    - "a compact and discriminative feature based on auditory summary statistics for acoustic scene classification"
    - "dcase 2018 challenge surrey cross task convolutional neural network baseline"
    - "beyond equal length snippets how long is sufficient to recognize an audio scene"

Connected through hyperedge HE-2 (induced by source paper "adaptive pooling operators for weakly labeled sound event detection"):
  with members:
    - "sonyc a system for the monitoring analysis and mitigation of urban noise pollution"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "a joint separation classification model for sound event detection of weakly labelled data"
    - "sound event detection using spatial features and convolutional recurrent neural network"
    - "look listen and learn"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"

Connected through hyperedge HE-3 (induced by source paper "dcase 2017 task 1 acoustic scene classification using shift invariant kernels and random features"):
  with members:
    - "learning audio sequence representations for acoustic event classification"
    - "look listen and learn"
    - "soundnet learning sound representations from unlabeled video"

Connected through hyperedge HE-4 (induced by source paper "towards a more efficient representation of imputation operators in tpot"):
  with members:
    - "evolving imputation strategies for missing data in classification problems with tpot"

Connected through hyperedge HE-5 (induced by source paper "an approach for self training audio event detectors using web data"):
  with members:
    - "weakly supervised scalable audio content analysis"
    - "audio event detection using weakly labeled data"
    - "sound representation and classification benchmark for domestic robots"

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)