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: "learning to model aspects of hearing perception using neural loss functions"
  Abstract: We present a framework to model the perceived quality of audio signals by combining convolutional architectures, with ideas from classical signal processing, and describe an approach to enhancing perceived acoustical quality. We demonstrate the approach by transforming the sound of an inexpensive musical with degraded sound quality to that of a high-quality musical instrument without the need for parallel data which is often hard to collect. We adapt the classical approach of a simple adaptive EQ filtering to the objective criterion learned by a neural architecture and optimize it to get the signal of our interest. Since we learn adaptive masks depending on the signal of interest as opposed to a fixed transformation for all the inputs, we show that shallow neural architectures can achieve the desired result. A simple constraint on the objective and the initialization helps us in avoiding adversarial examples, which otherwise would have produced noisy, unintelligible audio. We believe that the current framework proposed has enormous applications, in a variety of problems where one can learn a loss function depending on the problem, using a neural architecture and optimize it after it has been learned.

Connected through hyperedge HE-1 (induced by source paper "unsupervised learning of audio perception for robotics applications learning to project data to t sne umap space"):
  with members:
    - "neuralogram a deep neural network based representation for audio signals"
    - "end to end spoken language translation"
    - "audio linguistic embeddings for spoken sentences"
    - "neural style transfer for audio spectograms"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "conditional end to end audio transforms"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "look listen and learn"

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)