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: "onsets and frames dual objective piano transcription"
  Abstract: We consider the problem of transcribing polyphonic piano music with an emphasis on generalizing to unseen instruments. We use deep neural networks and propose a novel approach that predicts onsets and frames using both CNNs and LSTMs. This model predicts pitch onset events and then uses those predictions to condition framewise pitch predictions. During inference, we restrict the predictions from the framewise detector by not allowing a new note to start unless the onset detector also agrees that an onset for that pitch is present in the frame. We focus on improving onsets and offsets together instead of either in isolation as we believe it correlates better with human musical perception. This technique results in over a 100% relative improvement in note with offset score on the MAPS dataset.

Connected through hyperedge HE-1 (induced by source paper "music2dance dancenet for music driven dance generation"):
  with members:
    - "dancing to music"
    - "learning individual styles of conversational gesture"
    - "a neural temporal model for human motion prediction"
    - "listen to dance music driven choreography generation using autoregressive encoder decoder network"
    - "convolutional sequence to sequence model for human dynamics"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "weakly supervised deep recurrent neural networks for basic dance step generation"
    - "on human motion prediction using recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "music2dance music driven dance generation using wavenet"):
  with members:
    - "a neural temporal model for human motion prediction"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "listen to dance music driven choreography generation using autoregressive encoder decoder network"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "weakly supervised deep recurrent neural networks for basic dance step generation"
    - "on human motion prediction using recurrent neural networks"
    - "auto conditioned recurrent networks for extended complex human motion synthesis"
    - "madmom a new python audio and music signal processing library"

Connected through hyperedge HE-3 (induced by source paper "pop music transformer generating music with rhythm and harmony"):
  with members:
    - "adversarial learning for improved onsets and frames music transcription"
    - "encoding musical style with transformer autoencoders"
    - "lakhnes improving multi instrumental music generation with cross domain pre training"
    - "ctrl a conditional transformer language model for controllable generation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "this time with feeling learning expressive musical performance"
    - "a hierarchical latent vector model for learning long term structure in music"

Connected through hyperedge HE-4 (induced by source paper "the impact of audio input representations on neural network based music transcription"):
  with members:
    - "deep complex networks"
    - "on the potential of simple framewise approaches to piano transcription"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-5 (induced by source paper "modeling musical onset probabilities via neural distribution learning"):
  with members:
    - "dance dance convolution"

Connected through hyperedge HE-6 (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-7 (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"
    - "music transformer"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised temporal localization via occurrence count learning"):
  with members:
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"
    - "a joint detection classification model for audio tagging of weakly labelled data"

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.SD(Sound)