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: "deep ad hoc beamforming"
  Abstract: Although deep learning based speech enhancement methods have demonstrated good performance in adverse acoustic environments, their performance is strongly affected by the distance between the speech source and the microphones since speech signals fade quickly during the propagation through air. In this paper, we propose \textit{deep ad-hoc beamforming} to address the far field speech processing problem. It contains two novel components. First, it combines \textit{ad-hoc microphone arrays} with deep-learning-based multichannel speech enhancement, where an ad-hoc microphone array is a set of randomly distributed microphones collaborating with each other. This combination reduces the probability of the occurrence of far-field acoustic environments significantly. Second, it opens a new problem---\textit{channel selection}---to the deep-learning-based multichannel speech enhancement, and groups the microphones around the speech source to a local microphone array by a channel selection algorithm. The channel selection algorithm first predicts the quality of the received speech signal of each channel by a deep neural network. Then, it groups the microphones that have high speech quality and strong cross-channel signal correlation into a local microphone array. We developed several channel selection algorithms from the simplest one-best channel selection to a machine-learning-based channel selection. We conducted an extensive experiment in scenarios where the locations of the speech sources are far-field, random, and blind to the microphones. Results show that our method outperforms representative deep-learning-based speech enhancement methods by a large margin in both diffuse noise reverberant environments and point source noise reverberant environments.

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)