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: "music transformer"
  Abstract: Music relies heavily on repetition to build structure and meaning. Self-reference occurs on multiple timescales, from motifs to phrases to reusing of entire sections of music, such as in pieces with ABA structure. The Transformer (Vaswani et al., 2017), a sequence model based on self-attention, has achieved compelling results in many generation tasks that require maintaining long-range coherence. This suggests that self-attention might also be well-suited to modeling music. In musical composition and performance, however, relative timing is critically important. Existing approaches for representing relative positional information in the Transformer modulate attention based on pairwise distance (Shaw et al., 2018). This is impractical for long sequences such as musical compositions since their memory complexity for intermediate relative information is quadratic in the sequence length. We propose an algorithm that reduces their intermediate memory requirement to linear in the sequence length. This enables us to demonstrate that a Transformer with our modified relative attention mechanism can generate minute-long compositions (thousands of steps, four times the length modeled in Oore et al., 2018) with compelling structure, generate continuations that coherently elaborate on a given motif, and in a seq2seq setup generate accompaniments conditioned on melodies. We evaluate the Transformer with our relative attention mechanism on two datasets, JSB Chorales and Piano-e-Competition, and obtain state-of-the-art results on the latter.

Connected through hyperedge HE-1 (induced by source paper "stand alone self attention in vision models"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "local relation networks for image recognition"
    - "nas fpn learning scalable feature pyramid architecture for object detection"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "self attention with relative position representations"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"

Connected through hyperedge HE-2 (induced by source paper "attention augmented convolutional networks"):
  with members:
    - "the evolved transformer"
    - "bam bottleneck attention module"
    - "do better imagenet models transfer better"
    - "image transformer"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dropblock a regularization method for convolutional networks"
    - "self attention with relative position representations"
    - "backprop evolution"

Connected through hyperedge HE-3 (induced by source paper "neural shuffle exchange networks sequence processing in o n log n time"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "star transformer"
    - "universal transformers"
    - "generating long sequences with sparse transformers"
    - "tensor2tensor for neural machine translation"
    - "recent advances in neural program synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character level language modeling with deeper self attention"

Connected through hyperedge HE-4 (induced by source paper "score and lyrics free singing voice generation"):
  with members:
    - "dilated convolution with dilated gru for music source separation"
    - "data efficient voice cloning for neural singing synthesis"
    - "high fidelity speech synthesis with adversarial networks"
    - "adversarially trained end to end korean singing voice synthesis system"
    - "gansynth adversarial neural audio synthesis"
    - "an overview of lead and accompaniment separation in music"
    - "group normalization"
    - "revisiting singing voice detection a quantitative review and the future outlook"

Connected through hyperedge HE-5 (induced by source paper "toward interpretable music tagging with self attention"):
  with members:
    - "image transformer"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "compact generalized non local network"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "nsml meet the mlaas platform with a real world case study"
    - "non local neural networks"
    - "attention is all you need"
    - "onsets and frames dual objective piano transcription"

Connected through hyperedge HE-6 (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"
    - "audio to score alignment using transposition invariant features"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"

Connected through hyperedge HE-7 (induced by source paper "generating long sequences with sparse transformers"):
  with members:
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "generating wikipedia by summarizing long sequences"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "character level language modeling with deeper self attention"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "monotonic chunkwise attention"
    - "attention is all you need"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-8 (induced by source paper "improving polyphonic music models with feature rich encoding"):
  with members:
    - "lakhnes improving multi instrumental music generation with cross domain pre training"
    - "transformer xl attentive language models beyond a fixed length context"
    - "counterpoint by convolution"
    - "the nes music database a multi instrumental dataset with expressive performance attributes"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "attention is all you need"
    - "deepbach a steerable model for bach chorales generation"

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)