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: "sonyc a system for the monitoring analysis and mitigation of urban noise pollution"
  Abstract: We present the Sounds of New York City (SONYC) project, a smart cities initiative focused on developing a cyber-physical system for the monitoring, analysis and mitigation of urban noise pollution. Noise pollution is one of the topmost quality of life issues for urban residents in the U.S. with proven effects on health, education, the economy, and the environment. Yet, most cities lack the resources to continuously monitor noise and understand the contribution of individual sources, the tools to analyze patterns of noise pollution at city-scale, and the means to empower city agencies to take effective, data-driven action for noise mitigation. The SONYC project advances novel technological and socio-technical solutions that help address these needs. #R##N#SONYC includes a distributed network of both sensors and people for large-scale noise monitoring. The sensors use low-cost, low-power technology, and cutting-edge machine listening techniques, to produce calibrated acoustic measurements and recognize individual sound sources in real time. Citizen science methods are used to help urban residents connect to city agencies and each other, understand their noise footprint, and facilitate reporting and self-regulation. Crucially, SONYC utilizes big data solutions to analyze, retrieve and visualize information from sensors and citizens, creating a comprehensive acoustic model of the city that can be used to identify significant patterns of noise pollution. These data can be used to drive the strategic application of noise code enforcement by city agencies to optimize the reduction of noise pollution. The entire system, integrating cyber, physical and social infrastructure, forms a closed loop of continuous sensing, analysis and actuation on the environment. #R##N#SONYC provides a blueprint for the mitigation of noise pollution that can potentially be applied to other cities in the US and abroad.

Connected through hyperedge HE-1 (induced by source paper "specialized decision surface and disentangled feature for weakly supervised polyphonic sound event detection"):
  with members:
    - "sound event detection using graph laplacian regularization based on event co occurrence"
    - "weakly labelled audioset tagging with attention neural networks"
    - "large scale weakly labeled semi supervised sound event detection in domestic environments"
    - "attention based deep multiple instance learning"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "comparing the max and noisy or pooling functions in multiple instance learning for weakly supervised sequence learning tasks"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"

Connected through hyperedge HE-2 (induced by source paper "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"):
  with members:
    - "learning sound event classifiers from web audio with noisy labels"
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "audio tagging with noisy labels and minimal supervision"
    - "dcase 2018 challenge baseline with convolutional neural networks"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "automatic tagging using deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "adaptive pooling operators for weakly labeled sound event detection"):
  with members:
    - "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"
    - "a joint detection classification model for audio tagging of weakly labelled data"

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)