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: "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"
  Abstract: Robust geometric and semantic scene understanding is ever more important in many real-world applications such as autonomous driving and robotic navigation. In this paper, we propose a multi-task learning-based approach capable of jointly performing geometric and semantic scene understanding, namely depth prediction (monocular depth estimation and depth completion) and semantic scene segmentation. Within a single temporally constrained recurrent network, our approach uniquely takes advantage of a complex series of skip connections, adversarial training and the temporal constraint of sequential frame recurrence to produce consistent depth and semantic class labels simultaneously. Extensive experimental evaluation demonstrates the efficacy of our approach compared to other contemporary state-of-the-art techniques.

Connected through hyperedge HE-1 (induced by source paper "to complete or to estimate that is the question a multi task approach to depth completion and monocular depth estimation"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "geometry aware symmetric domain adaptation for monocular depth estimation"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"

Connected through hyperedge HE-2 (induced by source paper "a kings ransom for encryption ransomware classification using augmented one shot learning and bayesian approximation"):
  with members:
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "temporal graph offset reconstruction towards temporally robust graph representation learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bayesian layers a module for neural network uncertainty"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "one shot learning with memory augmented neural networks"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "multi task regression based learning for autonomous unmanned aerial vehicle flight control within unstructured outdoor environments"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep drone racing learning agile flight in dynamic environments"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "fast autonomous flight in gps denied and cluttered environments"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "unsupervised learning of depth and ego motion from video"
    - "aggressive deep driving model predictive control with a cnn cost model"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"

Connected through hyperedge HE-4 (induced by source paper "temporally coherent general dynamic scene reconstruction"):
  with members:
    - "learning 3d shape completion under weak supervision"
    - "specular to diffuse translation for multi view reconstruction"
    - "temporally coherent 4d reconstruction of complex dynamic scenes"
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"
    - "coherent motion segmentation in moving camera videos using optical flow orientations"

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)