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: "on the futility of learning complex frame level language models for chord recognition"
  Abstract: Chord recognition systems use temporal models to post-process frame-wise chord preditions from acoustic models. Traditionally, first-order models such as Hidden Markov Models were used for this task, with recent works suggesting to apply Recurrent Neural Networks instead. Due to their ability to learn longer-term dependencies, these models are supposed to learn and to apply musical knowledge, instead of just smoothing the output of the acoustic model. In this paper, we argue that learning complex temporal models at the level of audio frames is futile on principle, and that non-Markovian models do not perform better than their first-order counterparts. We support our argument through three experiments on the McGill Billboard dataset. The first two show 1) that when learning complex temporal models at the frame level, improvements in chord sequence modelling are marginal; and 2) that these improvements do not translate when applied within a full chord recognition system. The third, still rather preliminary experiment gives first indications that the use of complex sequential models for chord prediction at higher temporal levels might be more promising.

Connected through hyperedge HE-1 (induced by source paper "automatic chord recognition with higher order harmonic language modelling"):
  with members:
    - "a large scale study of language models for chord prediction"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "a fully convolutional deep auditory model for musical chord recognition"
    - "feature learning for chord recognition the deep chroma extractor"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "rethinking the inception architecture for computer vision"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "improved chord recognition by combining duration and harmonic language models"):
  with members:
    - "a large scale study of language models for chord prediction"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "a fully convolutional deep auditory model for musical chord recognition"
    - "feature learning for chord recognition the deep chroma extractor"
    - "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"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-3 (induced by source paper "a large scale study of language models for chord prediction"):
  with members:
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "feature learning for chord recognition the deep chroma extractor"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "efficient estimation of word representations in vector space"

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)