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: "surrey cvssp system for dcase2017 challenge task 4"
  Abstract: In this technique report, we present a bunch of methods for the task 4 of Detection and Classification of Acoustic Scenes and Events 2017 (DCASE2017) challenge. This task evaluates systems for the large-scale detection of sound events using weakly labeled training data. The data are YouTube video excerpts focusing on transportation and warnings due to their industry applications. There are two tasks, audio tagging and sound event detection from weakly labeled data. Convolutional neural network (CNN) and gated recurrent unit (GRU) based recurrent neural network (RNN) are adopted as our basic framework. We proposed a learnable gating activation function for selecting informative local features. Attention-based scheme is used for localizing the specific events in a weakly-supervised mode. A new batch-level balancing strategy is also proposed to tackle the data unbalancing problem. Fusion of posteriors from different systems are found effective to improve the performance. In a summary, we get 61% F-value for the audio tagging subtask and 0.73 error rate (ER) for the sound event detection subtask on the development set. While the official multilayer perceptron (MLP) based baseline just obtained 13.1% F-value for the audio tagging and 1.02 for the sound event detection.

Connected through hyperedge HE-1 (induced by source paper "sound event detection with depthwise separable and dilated convolutions"):
  with members:
    - "language modelling for sound event detection with teacher forcing and scheduled sampling"
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "network decoupling from regular to depthwise separable convolutions"
    - "listening for sirens locating and classifying acoustic alarms in city scenes"
    - "convolutional recurrent neural networks for bird audio detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "stacked convolutional and recurrent neural networks for music emotion recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "sound event detection by multitask learning of sound events and scenes with soft scene labels"):
  with members:
    - "joint analysis of acoustic events and scenes based on multitask learning"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-3 (induced by source paper "machine learning for music genre multifaceted review and experimentation with audioset"):
  with members:
    - "end to end speech recognition from the raw waveform"
    - "learning to separate object sounds by watching unlabeled video"
    - "audio based disambiguation of music genre tags"
    - "music mood detection based on audio and lyrics with deep neural net"
    - "the microsoft 2017 conversational speech recognition system"
    - "audio set classification with attention model a probabilistic perspective"
    - "reducing model complexity for dnn based large scale audio classification"
    - "multi label music genre classification from audio text and images using deep features"

Connected through hyperedge HE-4 (induced by source paper "hierarchical pooling structure for weakly labeled sound event detection"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "audio set classification with attention model a probabilistic perspective"
    - "convolutional sequence to sequence learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "sound event detection using graph laplacian regularization based on event co occurrence"):
  with members:
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "cnn architectures for large scale audio classification"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-6 (induced by source paper "weakly supervised representation learning for unsynchronized audio visual events"):
  with members:
    - "objects that sound"
    - "sound event detection using spatial features and convolutional recurrent neural network"
    - "see hear and read deep aligned representations"
    - "knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes"
    - "ambient sound provides supervision for visual learning"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "audio event detection using weakly labeled data"
    - "youtube 8m a large scale video classification benchmark"

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)