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: "creating a musical performance dataset for multimodal music analysis challenges insights and applications"
  Abstract: We introduce a dataset for facilitating audio-visual analysis of musical performances. The dataset comprises a number of simple multi-instrument musical pieces assembled from coordinated but separately recorded performances of individual tracks. For each piece, we provide the musical score in MIDI format, the audio recordings of the individual tracks, the audio and video recording of the assembled mixture, and ground-truth annotation files including frame-level and note-level transcriptions. We anticipate that the dataset will be useful for developing and evaluating multi-modal techniques for music source separation, transcription, score following, and performance analysis. We describe our methodology for the creation of this dataset, particularly highlighting our approaches for addressing the challenges involved in maintaining synchronization and naturalness. We briefly discuss the research questions that can be investigated with this dataset.

Connected through hyperedge HE-1 (induced by source paper "towards audio to scene image synthesis using generative adversarial network"):
  with members:
    - "creating a multitrack classical music performance dataset for multimodal music analysis challenges insights and applications"
    - "cgans with projection discriminator"
    - "spectral normalization for generative adversarial networks"
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
    - "cmcgan a uniform framework for cross modal visual audio mutual generation"
    - "learning word like units from joint audio visual analysis"
    - "deep cross modal audio visual generation"
    - "improved techniques for training gans"

Connected through hyperedge HE-2 (induced by source paper "end to end sound source separation conditioned on instrument labels"):
  with members:
    - "an overview of lead and accompaniment separation in music"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "class conditional embeddings for music source separation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "deep cross modal audio visual generation"):
  with members:
    - "multi style generative network for real time transfer"
    - "segan speech enhancement generative adversarial network"
    - "semi and weakly supervised semantic segmentation using generative adversarial network"
    - "learning deep representations of fine grained visual descriptions"
    - "semantic segmentation using adversarial networks"
    - "improved techniques for training gans"
    - "generative adversarial text to image synthesis"
    - "a comprehensive survey on cross modal retrieval"

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.MM(Multimedia)