human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "generationmania learning to semantically choreograph"):
  Abstract: Beatmania is a rhythm action game where players play the role of a DJ that performs music by pressing specific controller buttons to mix "Keysounds" (audio samples) at the correct time, unlike other rhythm action games such as Dance Dance Revolution. It has an active amateur Chart (Game stage) creation community, though chart authoring is considered a difficult and time consuming task. We present a deep neural network based process for automatically generating Beatmania charts for arbitrary pieces of music. Given a raw audio track of a song, we identify notes according to instrument, and use a neural network to classify each note as playable or non-playable. The final chart is produced by mapping playable notes to controls. We achieve a high level of performance, beating LSTM baselines.

Includes members:
  Member "procedural content generation via machine learning pcgml":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "increasing generality in machine learning through procedural content generation")
  Member "dance dance convolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "modeling musical onset probabilities via neural distribution learning")
  Member "wavenet a generative model for raw audio":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning")
  Member "sequence to sequence learning with neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

Question: Please tell me which class the source paper 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)