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: "transfer learning for music classification and regression tasks"
  Abstract: In this paper, we present a transfer learning approach for music classification and regression tasks. We propose to use a pre-trained convnet feature, a concatenated feature vector using the activations of feature maps of multiple layers in a trained convolutional network. We show how this convnet feature can serve as general-purpose music representation. In the experiments, a convnet is trained for music tagging and then transferred to other music-related classification and regression tasks. The convnet feature outperforms the baseline MFCC feature in all the considered tasks and several previous approaches that are aggregating MFCCs as well as low- and high-level music features.

Connected through hyperedge HE-1 (induced by source paper "toward interpretable music tagging with self attention"):
  with members:
    - "image transformer"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "compact generalized non local network"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "nsml meet the mlaas platform with a real world case study"
    - "music transformer"
    - "non local neural networks"
    - "attention is all you need"

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 "panns large scale pretrained audio neural networks for audio pattern recognition"):
  with members:
    - "musicnn pre trained convolutional neural networks for music audio tagging"
    - "bottom up broadcast neural network for music genre classification"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "weakly labelled audioset tagging with attention neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "general purpose tagging of freesound audio with audioset labels task description dataset and baseline"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "lakhnes improving multi instrumental music generation with cross domain pre training"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "counterpoint by convolution"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "the nes music database a multi instrumental dataset with expressive performance attributes"
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "deepj style specific music generation"
    - "hierarchical neural story generation"

Connected through hyperedge HE-5 (induced by source paper "bottom up broadcast neural network for music genre classification"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "randomly weighted cnns for music audio classification"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "audeep unsupervised learning of representations from audio with deep recurrent neural networks"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "dlr toward a deep learned rhythmic representation for music content analysis"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "prototypical metric transfer learning for continuous speech keyword spotting with limited training data"):
  with members:
    - "training neural audio classifiers with few data"
    - "prototypical networks for few shot learning"
    - "transfer learning for speech recognition on a budget"
    - "honk a pytorch reimplementation of convolutional neural networks for keyword spotting"
    - "matching networks for one shot learning"
    - "deep residual learning for image recognition"
    - "deep speech scaling up end to end speech recognition"
    - "deep metric learning using triplet network"

Connected through hyperedge HE-7 (induced by source paper "improving automatic jazz melody generation by transfer learning techniques"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "representation learning of music using artist labels"
    - "what makes imagenet good for transfer learning"
    - "song from pi a musically plausible network for pop music generation"
    - "music transcription modelling and composition using deep learning"
    - "deepbach a steerable model for bach chorales generation"

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"
    - "fma a dataset for music analysis"
    - "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.CV(Computer Vision and Pattern Recognition)