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: "end to end musical key estimation using a convolutional neural network"
  Abstract: We present an end-to-end system for musical key estimation, based on a convolutional neural network. The proposed system not only out-performs existing key estimation methods proposed in the academic literature; it is also capable of learning a unified model for diverse musical genres that performs comparably to existing systems specialised for specific genres. Our experiments confirm that different genres do differ in their interpretation of tonality, and thus a system tuned e.g. for pop music performs subpar on pieces of electronic music. They also reveal that such cross-genre setups evoke specific types of error (predicting the relative or parallel minor). However, using the data-driven approach proposed in this paper, we can train models that deal with multiple musical styles adequately, and without major losses in accuracy.

Connected through hyperedge HE-1 (induced by source paper "modeling music modality with a key class invariant pitch chroma cnn"):
  with members:
    - "frame level instrument recognition by timbre and pitch"
    - "polyphonic pitch tracking with deep layered learning"
    - "a data driven approach to mid level perceptual musical feature modeling"
    - "deep layered learning in mir"
    - "modeling majorness as a perceptual property in music from listener ratings"
    - "genre agnostic key classification with convolutional neural networks"
    - "a fully convolutional deep auditory model for musical chord recognition"
    - "feature learning for chord recognition the deep chroma extractor"

Connected through hyperedge HE-2 (induced by source paper "musical tempo and key estimation using convolutional neural networks with directional filters"):
  with members:
    - "genre agnostic key classification with convolutional neural networks"
    - "automatic tagging using deep convolutional neural networks"
    - "robust downbeat tracking using an ensemble of convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised deep recurrent neural networks for basic dance step generation"):
  with members:
    - "weakly supervised hierarchical text classification"
    - "audio super resolution using neural networks"
    - "madmom a new python audio and music signal processing library"
    - "audio event detection using weakly labeled data"
    - "generative choreography using deep learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "genre agnostic key classification with convolutional neural networks"):
  with members:
    - "a fully convolutional deep auditory model for musical chord recognition"
    - "feature learning for chord recognition the deep chroma extractor"
    - "why deep neural networks for function approximation"
    - "the power of depth for feedforward neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "striving for simplicity the all convolutional net"

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.LG(Machine Learning)