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 multitrack classical music performance dataset for multimodal music analysis challenges insights and applications"
  Abstract: We introduce a dataset for facilitating audio-visual analysis of music performances. The dataset comprises 44 simple multi-instrument classical music 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 describe our methodology for the creation of the dataset, particularly highlighting our approaches to address the challenges involved in maintaining synchronization and expressiveness. We demonstrate the high quality of synchronization achieved with our proposed approach by comparing the dataset with existing widely used music audio datasets. We anticipate that the dataset will be useful for the development and evaluation of existing music information retrieval (MIR) tasks, as well as for novel multimodal tasks. We benchmark two existing MIR tasks (multipitch analysis and score-informed source separation) on the dataset and compare them with other existing music audio datasets. In addition, we consider two novel multimodal MIR tasks (visually informed multipitch analysis and polyphonic vibrato analysis) enabled by the dataset and provide evaluation measurements and baseline systems for future comparisons (from our recent work). Finally, we propose several emerging research directions that the dataset enables.

Connected through hyperedge HE-1 (induced by source paper "the sound of motions"):
  with members:
    - "self supervised audio visual co segmentation"
    - "learning to localize sound source in visual scenes"
    - "end to end learning of motion representation for video understanding"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "self supervised generation of spatial audio for 360 video"
    - "learning to separate object sounds by watching unlabeled video"

Connected through hyperedge HE-2 (induced by source paper "cascade attention guided residue learning gan for cross modal translation"):
  with members:
    - "mirrorgan learning text to image generation by redescription"
    - "self supervised audio visual co segmentation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "gated gan adversarial gated networks for multi collection style transfer"
    - "speech2face learning the face behind a voice"
    - "video to video synthesis"
    - "towards audio to scene image synthesis using generative adversarial network"
    - "mc gan multi conditional generative adversarial network for image synthesis"

Connected through hyperedge HE-3 (induced by source paper "towards audio to scene image synthesis using generative adversarial network"):
  with members:
    - "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"
    - "creating a musical performance dataset for multimodal music analysis challenges insights and applications"
    - "improved techniques for training gans"

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)