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: "an individualized super gaussian single microphone speech enhancement for hearing aid users with smartphone as an assistive device"
  Abstract: In this letter, we derive a new super-Gaussian joint maximum  a posteriori  (SGJMAP) based single microphone speech enhancement gain function. The developed speech enhancement method is implemented on a smartphone, and this arrangement functions as an assistive device to hearing aids. We introduce a  tradeoff  parameter in the derived gain function that allows the smartphone user to customize their listening preference, by controlling the amount of noise suppression and speech distortion in real-time based on their level of hearing comfort perceived in noisy real-world acoustic environment. Objective quality and intelligibility measures show the effectiveness of the proposed method in comparison to benchmark techniques considered in this letter. Subjective results reflect the usefulness of the developed speech enhancement application in real-world noisy conditions at signal to noise ratio levels of –5, 0, and 5 dB.

Connected through hyperedge HE-1 (induced by source paper "the interspeech 2020 deep noise suppression challenge datasets subjective speech quality and testing framework"):
  with members:
    - "conv tasnet surpassing ideal time frequency magnitude masking for speech separation"
    - "a scalable noisy speech dataset and online subjective test framework"
    - "phasen a phase and harmonics aware speech enhancement network"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"

Connected through hyperedge HE-2 (induced by source paper "a computationally efficient and practically feasible two microphones blind speech separation method"):
  with members:
    - "adam a method for stochastic optimization"
    - "a general framework for online audio source separation"

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)