human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "modeling music modality with a key class invariant pitch chroma cnn"):
  Abstract: This paper presents a convolutional neural network (CNN) that uses input from a polyphonic pitch estimation system to predict perceived minor/major modality in music audio. The pitch activation input is structured to allow the first CNN layer to compute two pitch chromas focused on different octaves. The following layers perform harmony analysis across chroma and time scales. Through max pooling across pitch, the CNN becomes invariant with regards to the key class (i.e., key disregarding mode) of the music. A multilayer perceptron combines the modality activation output with spectral features for the final prediction. The study uses a dataset of 203 excerpts rated by around 20 listeners each, a small challenging data size requiring a carefully designed parameter sharing. With an R2 of about 0.71, the system clearly outperforms previous systems as well as individual human listeners. A final ablation study highlights the importance of using pitch activations processed across longer time scales, and using pooling to facilitate invariance with regards to the key class.

Includes members:
  Member "frame level instrument recognition by timbre and pitch":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning multi instrument classification with partial labels")
  Member "polyphonic pitch tracking with deep layered learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep polyphonic adsr piano note transcription")
  Member "a data driven approach to mid level perceptual musical feature modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "affective computing for large scale heterogeneous multimedia data a survey")
  Member "deep layered learning in mir":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "polyphonic pitch tracking with deep layered learning")
  Member "modeling majorness as a perceptual property in music from listener ratings":
    also connected through other hyperedges:
      (none)
  Member "genre agnostic key classification with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "musical tempo and key estimation using convolutional neural networks with directional filters")
  Member "end to end musical key estimation using a convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "musical tempo and key estimation using convolutional neural networks with directional filters")
  Member "a fully convolutional deep auditory model for musical chord recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a bi directional transformer for musical chord recognition")

Question: Please tell me which class the source paper 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)