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: "audio to score alignment using transposition invariant features"
  Abstract: Audio-to-score alignment is an important pre-processing step for in-depth analysis of classical music. In this paper, we apply novel transposition-invariant audio features to this task. These low-dimensional features represent local pitch intervals and are learned in an unsupervised fashion by a gated autoencoder. Our results show that the proposed features are indeed fully transposition-invariant and enable accurate alignments between transposed scores and performances. Furthermore, they can even outperform widely used features for audio-to-score alignment on `untransposed data', and thus are a viable and more flexible alternative to well-established features for music alignment and matching.

Connected through hyperedge HE-1 (induced by source paper "learning to traverse latent spaces for musical score inpainting"):
  with members:
    - "music generation by deep learning challenges and directions"
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "this time with feeling learning expressive musical performance"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "the machine learning algorithm as creative musical tool"
    - "a predictive model for music based on learned interval representations"
    - "music transformer"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"

Connected through hyperedge HE-2 (induced by source paper "learning complex basis functions for invariant representations of audio"):
  with members:
    - "gabor convolutional networks"
    - "learning transposition invariant interval features from symbolic music and audio"
    - "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-3 (induced by source paper "learning transposition invariant interval features from symbolic music and audio"):
  with members:
    - "a predictive model for music based on learned interval representations"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "on multi view feature learning"

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)