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 "lakhnes improving multi instrumental music generation with cross domain pre training"):
  Abstract: We are interested in the task of generating multi-instrumental music scores. The Transformer architecture has recently shown great promise for the task of piano score generation; here we adapt it to the multi-instrumental setting. Transformers are complex, high-dimensional language models which are capable of capturing long-term structure in sequence data, but require large amounts of data to fit. Their success on piano score generation is partially explained by the large volumes of symbolic data readily available for that domain. We leverage the recently-introduced NES-MDB dataset of four-instrument scores from an early video game sound synthesis chip (the NES), which we find to be well-suited to training with the Transformer architecture. To further improve the performance of our model, we propose a pre-training technique to leverage the information in a large collection of heterogeneous music, namely the Lakh MIDI dataset. Despite differences between the two corpora, we find that this transfer learning procedure improves both quantitative and qualitative performance for our primary task.

Includes members:
  Member "transformer xl attentive language models beyond a fixed length context":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "counterpoint by convolution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rl duet online music accompaniment generation using deep reinforcement learning")
  Member "the challenge of realistic music generation modelling raw audio at scale":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical autoregressive image models with auxiliary decoders")
  Member "enabling factorized piano music modeling and generation with the maestro dataset":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "asmd an automatic framework for compiling multimodal datasets")
  Member "the nes music database a multi instrumental dataset with expressive performance attributes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving polyphonic music models with feature rich encoding")
  Member "convolutional generative adversarial networks with binary neurons for polyphonic music generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dawson a domain adaptive few shot generation framework")
  Member "deepj style specific music generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mg vae deep chinese folk songs generation with specific regional styles")
  Member "hierarchical neural story generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation")

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)