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 "polyphonic music composition with lstm neural networks and reinforcement learning"):
  Abstract: In the domain of algorithmic music composition, machine learning-driven systems eliminate the need for carefully hand-crafting rules for composition. In particular, the capability of recurrent neural networks to learn complex temporal patterns lends itself well to the musical domain. Promising results have been observed across a number of recent attempts at music composition using deep RNNs. These approaches generally aim at first training neural networks to reproduce subsequences drawn from existing songs. Subsequently, they are used to compose music either at the audio sample-level or at the note-level. We designed a representation that divides polyphonic music into a small number of monophonic streams. This representation greatly reduces the complexity of the problem and eliminates an exponential number of probably poor compositions. On top of our LSTM neural network that learnt musical sequences in this representation, we built an RL agent that learnt to find combinations of songs whose joint dominance produced pleasant compositions. We present Amadeus, an algorithmic music composition system that composes music that consists of intricate melodies, basic chords, and even occasional contrapuntal sequences.

Includes members:
  Member "music generation by deep learning challenges and directions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to traverse latent spaces for musical score inpainting")
  Member "composing music with grammar argumented neural networks and note level encoding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "midinet a convolutional generative adversarial network for symbolic domain music generation")
  Member "midinet a convolutional generative adversarial network for symbolic domain music generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "music generation by deep learning challenges and directions")
  Member "song from pi a musically plausible network for pop music generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rl duet online music accompaniment generation using deep reinforcement learning")
  Member "deepbach a steerable model for bach chorales generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "music generation by deep learning challenges and directions")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "ai methods in algorithmic composition a comprehensive survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "music generation by deep learning challenges and directions")
  Member "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "music generation by deep learning challenges and directions")

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)