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: "deepc mvs deep confidence prediction for multi view stereo reconstruction"
  Abstract: Deep Neural Networks (DNNs) have the potential to improve the quality of image-based 3D reconstructions. A challenge which still remains is to utilize the potential of DNNs to improve 3D reconstructions from high-resolution image datasets as available by the ETH3D benchmark. In this paper, we propose a way to employ DNNs in the image domain to gain a significant quality improvement of geometric image based 3D reconstruction. This is achieved by utilizing confidence prediction networks which have been adapted to the Multi-View Stereo (MVS) case and are trained on automatically generated ground truth established by geometric error propagation. In addition to a semi-dense real-world ground truth dataset for training the DNN, we present a synthetic dataset to enlarge the training dataset. We demonstrate the utility of the confidence predictions for two essential steps within a 3D reconstruction pipeline: Firstly, to be used for outlier clustering and filtering and secondly to be used within a depth refinement step. The presented 3D reconstruction pipeline DeepC-MVS makes use of deep learning for an essential part in MVS from high-resolution images and the experimental evaluation on popular benchmarks demonstrates the achieved state-of-the-art quality in 3D reconstruction.

Connected through hyperedge HE-1 (induced by source paper "joint graph based depth refinement and normal estimation"):
  with members:
    - "learned collaborative stereo refinement"
    - "multi scale geometric consistency guided multi view stereo"
    - "deepmvs learning multi view stereopsis"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "detect replace refine deep structured prediction for pixel wise labeling"
    - "the fast bilateral solver"
    - "adam a method for stochastic optimization"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

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.CV(Computer Vision and Pattern Recognition)