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: "fma a dataset for music analysis"
  Abstract: We introduce the Free Music Archive (FMA), an open and easily accessible dataset suitable for evaluating several tasks in MIR, a field concerned with browsing, searching, and organizing large music collections. The community's growing interest in feature and end-to-end learning is however restrained by the limited availability of large audio datasets. The FMA aims to overcome this hurdle by providing 917 GiB and 343 days of Creative Commons-licensed audio from 106,574 tracks from 16,341 artists and 14,854 albums, arranged in a hierarchical taxonomy of 161 genres. It provides full-length and high-quality audio, pre-computed features, together with track- and user-level metadata, tags, and free-form text such as biographies. We here describe the dataset and how it was created, propose a train/validation/test split and three subsets, discuss some suitable MIR tasks, and evaluate some baselines for genre recognition. Code, data, and usage examples are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "multi modal music information retrieval augmenting audio analysis with visual computing for improved music video analysis"):
  with members:
    - "predicting time to failure of plasma etching equipment using machine learning"
    - "cross modal music retrieval and applications an overview of key methodologies"
    - "multi task music representation learning from multi label embeddings"
    - "fashion and apparel classification using convolutional neural networks"
    - "a multi modal deep neural network approach to bird song identification"
    - "large scale audio visual video analytics platform for forensic investigations of terroristic attacks"
    - "multi temporal resolution convolutional neural networks for acoustic scene classification"
    - "content based video music retrieval using soft intra modal structure constraint"

Connected through hyperedge HE-2 (induced by source paper "machine learning for music genre multifaceted review and experimentation with audioset"):
  with members:
    - "end to end speech recognition from the raw waveform"
    - "learning to separate object sounds by watching unlabeled video"
    - "audio based disambiguation of music genre tags"
    - "music mood detection based on audio and lyrics with deep neural net"
    - "the microsoft 2017 conversational speech recognition system"
    - "surrey cvssp system for dcase2017 challenge task 4"
    - "audio set classification with attention model a probabilistic perspective"
    - "reducing model complexity for dnn based large scale audio classification"

Connected through hyperedge HE-3 (induced by source paper "learning to adaptively scale recurrent neural networks"):
  with members:
    - "dilated recurrent neural networks"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "recurrent dropout without memory loss"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "improving neural language models with a continuous cache"
    - "wavenet a generative model for raw audio"
    - "pointer sentinel mixture models"

Connected through hyperedge HE-4 (induced by source paper "bppsa scaling back propagation by parallel scan algorithm"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "measuring the effects of data parallelism on neural network training"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "identity mappings in deep residual networks"
    - "optimizing performance of recurrent neural networks on gpus"
    - "compression of neural machine translation models via pruning"

Connected through hyperedge HE-5 (induced by source paper "gan based generation and automatic selection of explanations for neural networks"):
  with members:
    - "revisiting singing voice detection a quantitative review and the future outlook"
    - "methods for interpreting and understanding deep neural networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "improved training of wasserstein gans"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-6 (induced by source paper "learning discrete structures for graph neural networks"):
  with members:
    - "link prediction based on graph neural networks"
    - "unbiasing truncated backpropagation through time"
    - "learning deep generative models of graphs"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "neural message passing for quantum chemistry"
    - "learning graph representations with embedding propagation"
    - "towards poisoning of deep learning algorithms with back gradient optimization"

Connected through hyperedge HE-7 (induced by source paper "zero shot learning for audio based music classification and tagging"):
  with members:
    - "multi label zero shot human action recognition via joint latent embedding"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "representation learning of music using artist labels"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "multi label zero shot learning via concept embedding"
    - "end to end localization and ranking for relative attributes"
    - "convolutional recurrent neural networks for music classification"
    - "latent embeddings for zero shot classification"

Connected through hyperedge HE-8 (induced by source paper "enhancing music features by knowledge transfer from user item log data"):
  with members:
    - "deep content user embedding model for music recommendation"
    - "kapre on gpu audio preprocessing layers for a quick implementation of deep neural network models with keras"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "transfer learning for music classification and regression tasks"
    - "automatic tagging using deep convolutional neural networks"
    - "convolutional recurrent neural networks for music classification"
    - "adam a method for stochastic optimization"
    - "fitnets hints for thin deep nets"

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.SD(Sound)