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: "an experimental analysis of the entanglement problem in neural network based music transcription systems"
  Abstract: Several recent polyphonic music transcription systems have utilized deep neural networks to achieve state of the art results on various benchmark datasets, pushing the envelope on framewise and note-level performance measures. Unfortunately we can observe a sort of glass ceiling effect. To investigate this effect, we provide a detailed analysis of the particular kinds of errors that state of the art deep neural transcription systems make, when trained and tested on a piano transcription task. We are ultimately forced to draw a rather disheartening conclusion: the networks seem to learn combinations of notes, and have a hard time generalizing to unseen combinations of notes. Furthermore, we speculate on various means to alleviate this situation.

Connected through hyperedge HE-1 (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"
    - "progressive growing of gans for improved quality stability and variation"
    - "onsets and frames dual objective piano transcription"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-2 (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"
    - "rhythm transcription of polyphonic piano music based on merged output hmm for multiple voices"
    - "on the potential of simple framewise approaches to piano transcription"
    - "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-3 (induced by source paper "between homomorphic signal processing and deep neural networks constructing deep algorithms for polyphonic music transcription"):
  with members:
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "on the potential of simple framewise approaches to piano transcription"
    - "understanding convolutional neural networks with a mathematical model"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "on the expressive power of deep learning a tensor analysis"
    - "generative modeling of convolutional neural networks"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-4 (induced by source paper "music transcription by deep learning with data and artificial semantic augmentation"):
  with members:
    - "user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities"
    - "augmented coaching ecosystem for non obtrusive adaptive personalized elderly care on the basis of cloud fog dew computing paradigm"
    - "automatized generation of alphabets of symbols"
    - "comparative performance analysis of neural networks architectures on h2o platform for various activation functions"
    - "slice to volume medical image registration a survey"
    - "comparative analysis of open source frameworks for machine learning with use case in single threaded and multi threaded modes"

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)