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: "regression and classification for direction of arrival estimation with convolutional recurrent neural networks"
  Abstract: We present a novel learning-based approach to estimate the direction-of-arrival (DOA) of a sound source using a convolutional recurrent neural network (CRNN) trained via regression on synthetic data and Cartesian labels. We also describe an improved method to generate synthetic data to train the neural network using state-of-the-art sound propagation algorithms that model specular as well as diffuse reflections of sound. We compare our model against three other CRNNs trained using different formulations of the same problem: classification on categorical labels, and regression on spherical coordinate labels. In practice, our model achieves up to 43% decrease in angular error over prior methods. The use of diffuse reflection results in 34% and 41% reduction in angular prediction errors on LOCATA and SOFA datasets, respectively, over prior methods based on image-source methods. Our method results in an additional 3% error reduction over prior schemes that use classification based networks, and we use 36% fewer network parameters.

Connected through hyperedge HE-1 (induced by source paper "scene aware audio rendering via deep acoustic analysis"):
  with members:
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "data augmentation and deep convolutional neural networks for blind room acoustic parameter estimation"
    - "impulse response data augmentation and deep neural networks for blind room acoustic parameter estimation"
    - "deeplight learning illumination for unconstrained mobile mixed reality"
    - "low frequency compensated synthetic impulse responses for improved far field speech recognition"
    - "improving reverberant speech training using diffuse acoustic simulation"
    - "scene aware audio for 360 videos"
    - "codeslam learning a compact optimisable representation for dense visual slam"

Connected through hyperedge HE-2 (induced by source paper "improving reverberant speech training using diffuse acoustic simulation"):
  with members:
    - "scene aware audio rendering via deep acoustic analysis"
    - "low frequency compensated synthetic impulse responses for improved far field speech recognition"
    - "interactive sound rendering on mobile devices using ray parameterized reverberation filters"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "the microsoft 2016 conversational speech recognition system"
    - "deep clustering discriminative embeddings for segmentation and separation"

Connected through hyperedge HE-3 (induced by source paper "low frequency compensated synthetic impulse responses for improved far field speech recognition"):
  with members:
    - "scene aware audio rendering via deep acoustic analysis"
    - "improving reverberant speech training using diffuse acoustic simulation"
    - "interactive sound rendering on mobile devices using ray parameterized reverberation filters"

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)