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: "on the potential of simple framewise approaches to piano transcription"
  Abstract: In an attempt at exploring the limitations of simple approaches to the task of piano transcription (as usually defined in MIR), we conduct an in-depth analysis of neural network-based framewise transcription. We systematically compare different popular input representations for transcription systems to determine the ones most suitable for use with neural networks. Exploiting recent advances in training techniques and new regularizers, and taking into account hyper-parameter tuning, we show that it is possible, by simple bottom-up frame-wise processing, to obtain a piano transcriber that outperforms the current published state of the art on the publicly available MAPS dataset -- without any complex post-processing steps. Thus, we propose this simple approach as a new baseline for this dataset, for future transcription research to build on and improve.

Connected through hyperedge HE-1 (induced by source paper "the impact of audio input representations on neural network based music transcription"):
  with members:
    - "onsets and frames dual objective piano transcription"
    - "deep complex networks"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised temporal localization via occurrence count learning"):
  with members:
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "onsets and frames dual objective piano transcription"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"

Connected through hyperedge HE-3 (induced by source paper "adversarial learning for improved onsets and frames music transcription"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "neural music synthesis for flexible timbre control"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "generative adversarial networks an overview"
    - "a style based generator architecture for generative adversarial networks"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-4 (induced by source paper "towards interpretable polyphonic transcription with invertible neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "model agnostic supervised local explanations"
    - "excessive invariance causes adversarial vulnerability"
    - "i revnet deep invertible networks"
    - "generative modeling using the sliced wasserstein distance"
    - "an experimental analysis of the entanglement problem in neural network based music transcription systems"
    - "progressive growing of gans for improved quality stability and variation"
    - "onsets and frames dual objective piano transcription"

Connected through hyperedge HE-5 (induced by source paper "nnaudio an on the fly gpu audio to spectrogram conversion toolbox using 1d convolution neural networks"):
  with members:
    - "adversarial learning for improved onsets and frames music transcription"
    - "towards interpretable polyphonic transcription with invertible neural networks"
    - "an attention mechanism for musical instrument recognition"
    - "zero shot learning for audio based music classification and tagging"
    - "learning soft attention models for tempo invariant audio sheet music retrieval"
    - "a bi directional transformer for musical chord recognition"
    - "cover detection using dominant melody embeddings"
    - "kapre on gpu audio preprocessing layers for a quick implementation of deep neural network models with keras"

Connected through hyperedge HE-6 (induced by source paper "learning soft attention models for tempo invariant audio sheet music retrieval"):
  with members:
    - "deep polyphonic adsr piano note transcription"
    - "cross modal music retrieval and applications an overview of key methodologies"
    - "attention as a perspective for learning tempo invariant audio queries"
    - "end to end cross modality retrieval with cca projections and pairwise ranking loss"
    - "look listen and learn"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "a holistic approach to polyphonic music transcription with neural networks"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "an experimental analysis of the entanglement problem in neural network based music transcription systems"
    - "rhythm transcription of polyphonic piano music based on merged output hmm for multiple voices"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "deep polyphonic adsr piano note transcription"):
  with members:
    - "polyphonic pitch tracking with deep layered learning"
    - "onsets and frames dual objective piano transcription"
    - "madmom a new python audio and music signal processing library"
    - "fast and accurate deep network learning by exponential linear units elus"

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)