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: "counterpoint by convolution"
  Abstract: Machine learning models of music typically break up the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end. On the contrary, human composers write music in a nonlinear fashion, scribbling motifs here and there, often revisiting choices previously made. In order to better approximate this process, we train a convolutional neural network to complete partial musical scores, and explore the use of blocked Gibbs sampling as an analogue to rewriting. Neither the model nor the generative procedure are tied to a particular causal direction of composition. Our model is an instance of orderless NADE (Uria et al., 2014), which allows more direct ancestral sampling. However, we find that Gibbs sampling greatly improves sample quality, which we demonstrate to be due to some conditional distributions being poorly modeled. Moreover, we show that even the cheap approximate blocked Gibbs procedure from Yao et al. (2014) yields better samples than ancestral sampling, based on both log-likelihood and human evaluation.

Connected through hyperedge HE-1 (induced by source paper "rl duet online music accompaniment generation using deep reinforcement learning"):
  with members:
    - "the bach doodle approachable music composition with machine learning at scale"
    - "gansynth adversarial neural audio synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "deep learning techniques for music generation a survey"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "an actor critic algorithm for sequence prediction"
    - "c rnn gan continuous recurrent neural networks with adversarial training"

Connected through hyperedge HE-2 (induced by source paper "modeling musical structure with artificial neural networks"):
  with members:
    - "high level control of drum track generation using learned patterns of rhythmic interaction"
    - "learning complex basis functions for invariant representations of audio"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "a predictive model for music based on learned interval representations"
    - "improving content invariance in gated autoencoders for 2d and 3d object rotation"
    - "text based lstm networks for automatic music composition"
    - "deepbach a steerable model for bach chorales generation"
    - "spatial transformer networks"

Connected through hyperedge HE-3 (induced by source paper "dawson a domain adaptive few shot generation framework"):
  with members:
    - "metalgan multi domain label less image synthesis using cgans and meta learning"
    - "figr few shot image generation with reptile"
    - "this time with feeling learning expressive musical performance"
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "sample efficient adaptive text to speech"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "variational memory addressing in generative models"
    - "online learning with gated linear networks"

Connected through hyperedge HE-4 (induced by source paper "fair generative modeling via weak supervision"):
  with members:
    - "towards gan benchmarks which require generalization"
    - "scalable fair clustering"
    - "fair clustering through fairlets"
    - "quantitatively evaluating gans with divergences proposed for training"
    - "datasheets for datasets"
    - "what is the effect of importance weighting in deep learning"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "learning beyond human expertise with generative models for dental restorations"

Connected through hyperedge HE-5 (induced by source paper "encoding musical style with transformer autoencoders"):
  with members:
    - "exploring conditioning for generative music systems with human interpretable controls"
    - "learning to groove with inverse sequence transformations"
    - "improving automatic jazz melody generation by transfer learning techniques"
    - "this time with feeling learning expressive musical performance"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "self attention with relative position representations"
    - "discrete autoencoders for sequence models"
    - "a hierarchical latent vector model for learning long term structure in music"

Connected through hyperedge HE-6 (induced by source paper "learning to groove with inverse sequence transformations"):
  with members:
    - "video to video synthesis"
    - "predicting expressive speaking style from text in end to end speech synthesis"
    - "this time with feeling learning expressive musical performance"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "hierarchical neural story generation"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "semi supervised training for improving data efficiency in end to end speech synthesis"

Connected through hyperedge HE-7 (induced by source paper "lakhnes improving multi instrumental music generation with cross domain pre training"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "the nes music database a multi instrumental dataset with expressive performance attributes"
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "deepj style specific music generation"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"

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"
    - "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"
    - "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)