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: "learning transposition invariant interval features from symbolic music and audio"
  Abstract: Many music theoretical constructs (such as scale types, modes, cadences, and chord types) are defined in terms of pitch intervals---relative distances between pitches. Therefore, when computer models are employed in music tasks, it can be useful to operate on interval representations rather than on the raw musical surface. Moreover, interval representations are transposition-invariant, valuable for tasks like audio alignment, cover song detection and music structure analysis. We employ a gated autoencoder to learn fixed-length, invertible and transposition-invariant interval representations from polyphonic music in the symbolic domain and in audio. An unsupervised training method is proposed yielding an organization of intervals in the representation space which is musically plausible. Based on the representations, a transposition-invariant self-similarity matrix is constructed and used to determine repeated sections in symbolic music and in audio, yielding competitive results in the MIREX task "Discovery of Repeated Themes and Sections".

Connected through hyperedge HE-1 (induced by source paper "learning complex basis functions for invariant representations of audio"):
  with members:
    - "gabor convolutional networks"
    - "audio to score alignment using transposition invariant features"
    - "oriented response networks"
    - "deformable convolutional networks"
    - "end to end learning for music audio tagging at scale"
    - "transform invariant auto encoder"
    - "harmonic networks deep translation and rotation equivariance"
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "high level control of drum track generation using learned patterns of rhythmic interaction"):
  with members:
    - "audio style transfer"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "deepj style specific music generation"
    - "lead sheet generation and arrangement by conditional generative adversarial network"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "a predictive model for music based on learned interval representations"
    - "self normalizing neural networks"
    - "deep learning techniques for music generation a survey"

Connected through hyperedge HE-3 (induced by source paper "a predictive model for music based on learned interval representations"):
  with members:
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-4 (induced by source paper "audio to score alignment using transposition invariant features"):
  with members:
    - "robust joint alignment of multiple versions of a piece of music"

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)