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: "one deep music representation to rule them all a comparative analysis of different representation learning strategies"
  Abstract: Inspired by the success of deploying deep learning in the fields of Computer Vision and Natural Language Processing, this learning paradigm has also found its way into the field of Music Information Retrieval. In order to benefit from deep learning in an effective, but also efficient manner, deep transfer learning has become a common approach. In this approach, it is possible to reuse the output of a pre-trained neural network as the basis for a new, yet unseen learning task. The underlying hypothesis is that if the initial and new learning tasks show commonalities and are applied to the same type of data (e.g. music audio), the generated deep representation of the data is also informative for the new task. Since, however, most of the networks used to generate deep representations are trained using a single initial learning task, the validity of the above hypothesis is questionable for an arbitrary new learning task. In this paper we present the results of our investigation of what the best ways are to generate deep representations for the data and learning tasks in the music domain. We conducted this investigation via an extensive empirical study that involves multiple learning tasks, as well as multiple deep learning architectures with varying levels of information sharing between tasks, in order to learn music representations. We then validate these representations considering multiple unseen learning tasks for evaluation. The results of our experiments yield several insights on how to approach the design of methods for learning widely deployable deep data representations in the music domain.

Connected through hyperedge HE-1 (induced by source paper "generative autoregressive networks for 3d dancing move synthesis from music"):
  with members:
    - "modeling human motion with quaternion based neural networks"
    - "listen to dance music driven choreography generation using autoregressive encoder decoder network"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "sample level cnn architectures for music auto tagging using raw waveforms"
    - "text2action generative adversarial synthesis from language to action"
    - "wavenet a generative model for raw audio"
    - "generative choreography using deep learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "multitask learning for polyphonic piano transcription a case study"):
  with members:
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "onsets and frames dual objective piano transcription"

Connected through hyperedge HE-3 (induced by source paper "randomly weighted cnns for music audio classification"):
  with members:
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "deep image prior"
    - "dlr toward a deep learned rhythmic representation for music content analysis"
    - "look listen and learn"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "multi label music genre classification from audio text and images using deep features"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"

Connected through hyperedge HE-4 (induced by source paper "large scale audio visual video analytics platform for forensic investigations of terroristic attacks"):
  with members:
    - "multi temporal resolution convolutional neural networks for acoustic scene classification"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "kapre on gpu audio preprocessing layers for a quick implementation of deep neural network models with keras"
    - "simple online and realtime tracking with a deep association metric"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "sound event detection in multichannel audio using spatial and harmonic features"
    - "yolo9000 better faster stronger"
    - "spatially supervised recurrent convolutional neural networks for visual object tracking"

Connected through hyperedge HE-5 (induced by source paper "transfer learning of artist group factors to musical genre classification"):
  with members:
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "representation learning of music using artist labels"
    - "fma a dataset for music analysis"
    - "theano a python framework for fast computation of mathematical expressions"
    - "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"
    - "heterogeneous multi task learning for human pose estimation with deep convolutional neural network"

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.NE(Neural and Evolutionary Computing)