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: "towards generating ambisonics using audio visual cue for virtual reality"
  Abstract: Ambisonics i.e., a full-sphere surround sound, is quintessential with 360° visual content to provide a realistic virtual reality (VR) experience. While 360° visual content capture gained a tremendous boost recently, the estimation of corresponding spatial sound is still challenging due to the required sound-field microphones or information about the sound-source locations. In this paper, we introduce a novel problem of generating Ambisonics in 360° videos using the audiovisual cue. With this aim, firstly, a novel 360° audio-visual video dataset of 265 videos is introduced with annotated sound-source locations. Secondly, a pipeline is designed for an automatic Ambisonic estimation problem. Benefiting from the deep learning based audiovisual feature-embedding and prediction modules, our pipeline estimates the 3D sound-source locations and further use such locations to encode to the B-format. To benchmark our dataset and pipeline, we additionally propose evaluation criteria to investigate the performance using different 360° input representations. Our results demonstrate the efficacy of the proposed pipeline and open up a new area of research in 360° audio-visual analysis for future investigations.

Connected through hyperedge HE-1 (induced by source paper "super resolution of omnidirectional images using adversarial learning"):
  with members:
    - "deep learning for image super resolution a survey"
    - "colornet estimating colorfulness in natural images"
    - "the relativistic discriminator a key element missing from standard gan"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "toward low latency and ultra reliable virtual reality"
    - "learning a single convolutional super resolution network for multiple degradations"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-2 (induced by source paper "dublincity annotated lidar point cloud and its applications"):
  with members:
    - "so net self organizing network for point cloud analysis"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "paris lille 3d a large and high quality ground truth urban point cloud dataset for automatic segmentation and classification"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "torontocity seeing the world with a million eyes"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-3 (induced by source paper "colornet estimating colorfulness in natural images"):
  with members:
    - "nima neural image assessment"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)