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: "polyphonic music generation by modeling temporal dependencies using a rnn dbn"
  Abstract: In this paper, we propose a generic technique to model temporal dependencies and sequences using a combination of a recurrent neural network and a Deep Belief Network. Our technique, RNN-DBN, is an amalgamation of the memory state of the RNN that allows it to provide temporal information and a multi-layer DBN that helps in high level representation of the data. This makes RNN-DBNs ideal for sequence generation. Further, the use of a DBN in conjunction with the RNN makes this model capable of significantly more complex data representation than a Restricted Boltzmann Machine (RBM). We apply this technique to the task of polyphonic music generation.

Connected through hyperedge HE-1 (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"
    - "music transformer"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "counterpoint by convolution"):
  with members:
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "neural autoregressive distribution estimation"
    - "style imitation and chord invention in polyphonic music with exponential families"
    - "wavenet a generative model for raw audio"
    - "deepbach a steerable model for bach chorales generation"
    - "pixel recurrent neural networks"
    - "a neural algorithm of artistic style"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "the bach doodle approachable music composition with machine learning at scale"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "counterpoint by convolution"
    - "a functional taxonomy of music generation systems"
    - "deep learning techniques for music generation a survey"
    - "neural autoregressive distribution estimation"
    - "sequence tutor conservative fine tuning of sequence generation models with kl control"
    - "style imitation and chord invention in polyphonic music with exponential families"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-4 (induced by source paper "sequential context encoding for duplicate removal"):
  with members:
    - "path aggregation network for instance segmentation"
    - "soft nms improving object detection with one line of code"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "relation networks for object detection"
    - "deformable convolutional networks"
    - "learning non maximum suppression"
    - "spatial memory for context reasoning in object detection"

Connected through hyperedge HE-5 (induced by source paper "conditioning deep generative raw audio models for structured automatic music"):
  with members:
    - "a neural parametric singing synthesizer"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "deep learning for music"
    - "youtube 8m a large scale video classification benchmark"
    - "deepbach a steerable model for bach chorales generation"
    - "pixel recurrent neural networks"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-6 (induced by source paper "composing music with grammar argumented neural networks and note level encoding"):
  with members:
    - "pixel recurrent neural networks"
    - "a neural algorithm of artistic style"
    - "draw a recurrent neural network for image generation"
    - "adam a method for stochastic optimization"
    - "ai methods in algorithmic composition a comprehensive survey"
    - "generating sequences with recurrent neural networks"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-7 (induced by source paper "deep learning techniques for music generation a survey"):
  with members:
    - "music generation by deep learning challenges and directions"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "conditioning deep generative raw audio models for structured automatic music"
    - "deepj style specific music generation"
    - "music style transfer issues a position paper"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "the machine learning algorithm as creative musical tool"
    - "music style transfer a position paper"

Connected through hyperedge HE-8 (induced by source paper "polyphonic music generation with sequence generative adversarial networks"):
  with members:
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "adversarially regularized autoencoders"
    - "deep learning techniques for music generation a survey"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "unrolled generative adversarial networks"
    - "c rnn gan continuous recurrent neural networks with adversarial training"
    - "deep learning in bioinformatics"
    - "song from pi a musically plausible network for pop music 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)