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: "joint time frequency scattering for audio classification"
  Abstract: We introduce the joint time-frequency scattering transform, a time shift invariant descriptor of time-frequency structure for audio classification. It is obtained by applying a two-dimensional wavelet transform in time and log-frequency to a time-frequency wavelet scalogram. We show that this descriptor successfully characterizes complex time-frequency phenomena such as time-varying filters and frequency modulated excitations. State-of-the-art results are achieved for signal reconstruction and phone segment classification on the TIMIT dataset.

Connected through hyperedge HE-1 (induced by source paper "robust sound event detection in bioacoustic sensor networks"):
  with members:
    - "learning to detect dysarthria from raw speech"
    - "attention based end to end models for small footprint keyword spotting"
    - "recognizing birds from sound the 2018 birdclef baseline system"
    - "convolutional recurrent neural networks for bird audio detection"
    - "densely connected cnns for bird audio detection"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "deformable convolutional networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"

Connected through hyperedge HE-2 (induced by source paper "gabor frames and deep scattering networks in audio processing"):
  with members:
    - "machines listening to music the role of signal representations in learning from music"
    - "understanding deep learning requires rethinking generalization"
    - "deep convolutional neural networks on cartoon functions"
    - "discrete deep feature extraction a theory and new architectures"
    - "deep convolutional neural networks based on semi discrete frames"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "the shape of remixxxes to come audio texture synthesis with time frequency scattering"):
  with members:
    - "on time frequency scattering and computer music"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "machine listening intelligence"
    - "fader networks manipulating images by sliding attributes"
    - "wavelet scattering on the pitch spiral"
    - "bitwise neural networks"
    - "idealized computational models for auditory receptive fields"
    - "deep scattering spectrum"

Connected through hyperedge HE-4 (induced by source paper "extended playing techniques the next milestone in musical instrument recognition"):
  with members:
    - "classification with joint time frequency scattering"
    - "deep convolutional neural networks for predominant instrument recognition in polyphonic music"
    - "deep convolutional networks on the pitch spiral for musical instrument recognition"
    - "deep scattering spectrum"
    - "a survey on metric learning for feature vectors and structured data"

Connected through hyperedge HE-5 (induced by source paper "wavelet scattering on the pitch spiral"):
  with members:
    - "deep scattering spectrum"

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)