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: "towards score following in sheet music images"
  Abstract: This paper addresses the matching of short music audio snippets to the corresponding pixel location in images of sheet music. A system is presented that simultaneously learns to read notes, listens to music and matches the currently played music to its corresponding notes in the sheet. It consists of an end-to-end multi-modal convolutional neural network that takes as input images of sheet music and spectrograms of the respective audio snippets. It learns to predict, for a given unseen audio snippet (covering approximately one bar of music), the corresponding position in the respective score line. Our results suggest that with the use of (deep) neural networks -- which have proven to be powerful image processing models -- working with sheet music becomes feasible and a promising future research direction.

Connected through hyperedge HE-1 (induced by source paper "audio conditioned u net for position estimation in full sheet images"):
  with members:
    - "learning to listen read and follow score following as a reinforcement learning game"
    - "optical music recognition with convolutional sequence to sequence models"
    - "film visual reasoning with a general conditioning layer"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "end to end cross modality retrieval with cca projections and pairwise ranking loss"):
  with members:
    - "theano a python framework for fast computation of mathematical expressions"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "order embeddings of images and language"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "unifying visual semantic embeddings with multimodal neural language models"

Connected through hyperedge HE-3 (induced by source paper "learning to listen read and follow score following as a reinforcement learning game"):
  with members:
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "asynchronous methods for deep reinforcement learning"
    - "benchmarking deep reinforcement learning for continuous control"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "multi label music genre classification from audio text and images using deep features"):
  with members:
    - "automatic tagging using deep convolutional neural networks"
    - "convolutional recurrent neural networks for music classification"
    - "image based recommendations on styles and substitutes"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"
    - "some improvements on deep convolutional neural network based image classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "learning audio sheet music correspondences for score identification and offline alignment"):
  with members:
    - "end to end cross modality retrieval with cca projections and pairwise ranking loss"
    - "on the potential of simple framewise approaches to piano transcription"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "unifying visual semantic embeddings with multimodal neural language models"

Connected through hyperedge HE-6 (induced by source paper "towards end to end audio sheet music retrieval"):
  with members:
    - "fast and accurate deep network learning by exponential linear units elus"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "live score following on sheet music images"):
  with members:
    - "madmom a new python audio and music signal processing library"

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.LG(Machine Learning)