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: "capsule routing for sound event detection"
  Abstract: The detection of acoustic scenes is a challenging problem in which environmental sound events must be detected from a given audio signal. This includes classifying the events as well as estimating their onset and offset times. We approach this problem with a neural network architecture that uses the recently-proposed capsule routing mechanism. A capsule is a group of activation units representing a set of properties for an entity of interest, and the purpose of routing is to identify part-whole relationships between capsules. That is, a capsule in one layer is assumed to belong to a capsule in the layer above in terms of the entity being represented. Using capsule routing, we wish to train a network that can learn global coherence implicitly, thereby improving generalization performance. Our proposed method is evaluated on Task 4 of the DCASE 2017 challenge. Results show that classification performance is state-of-the-art, achieving an F-score of 58.6%. In addition, overfitting is reduced considerably compared to other architectures.

Connected through hyperedge HE-1 (induced by source paper "duration robust weakly supervised sound event detection"):
  with members:
    - "cosine similarity penalty to discriminate sound classes in weakly supervised sound event detection"
    - "specialized decision surface and disentangled feature for weakly supervised polyphonic sound event detection"
    - "connectionist temporal localization for sound event detection with sequential labeling"
    - "large scale weakly labeled semi supervised sound event detection in domestic environments"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "a light cnn for deep face representation with noisy labels"

Connected through hyperedge HE-2 (induced by source paper "improving performance and inference on audio classification tasks using capsule networks"):
  with members:
    - "brain tumor type classification via capsule networks"
    - "novel deep learning model for traffic sign detection using capsule networks"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "dynamic routing between capsules"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "musan a music speech and noise corpus"

Connected through hyperedge HE-3 (induced by source paper "duration robust sound event detection"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "connectionist temporal localization for sound event detection with sequential labeling"
    - "polyphonic audio tagging with sequentially labelled data using crnn with learnable gated linear units"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "language modeling with gated convolutional networks"
    - "a light cnn for deep face representation with noisy labels"

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)