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: "j mod 2 joint monocular obstacle detection and depth estimation"
  Abstract: In this letter, we propose an end-to-end deep architecture that jointly learns to detect obstacles and estimate their depth for MAV flight applications. Most of the existing approaches rely either on Visual simultaneous localization and mapping (SLAM) systems or on depth estimation models to build three-dimensional maps and detect obstacles. However, for the task of avoiding obstacles this level of complexity is not required. Recent works have proposed multitask at its first ocurrence in the text."?> architectures to perform both scene understanding and depth estimation. We follow their path and propose a specific architecture to jointly estimate depth and obstacles, without the need to compute a global map, but maintaining compatibility with a global SLAM system if needed. The network architecture is devised to jointly exploit the information learned from the obstacle detection task, which produces reliable bounding boxes, and the depth estimation one, increasing the robustness of both to scenario changes. We call this architecture J-MOD2. We test the effectiveness of our approach with experiments on sequences with different appearance and focal lengths and compare it to SotA multitask methods that perform both semantic segmentation and depth estimation. In addition, we show the integration in a full system using a set of simulated navigation experiments, where a micro aerial vehicle explores an unknown scenario and plans safe trajectories by using our detection model.

Connected through hyperedge HE-1 (induced by source paper "sparse to continuous enhancing monocular depth estimation using occupancy maps"):
  with members:
    - "from big to small multi scale local planar guidance for monocular depth estimation"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "unsupervised learning of depth and ego motion from video"
    - "sparsity invariant cnns"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-2 (induced by source paper "memory based deep reinforcement learning for obstacle avoidance in uav with limited environment knowledge"):
  with members:
    - "began boundary equilibrium generative adversarial networks"
    - "obstacle avoidance through deep networks based intermediate perception"
    - "unsupervised learning of depth and ego motion from video"
    - "regret minimization for partially observable deep reinforcement learning"
    - "semi supervised deep learning for monocular depth map prediction"
    - "fast robust monocular depth estimation for obstacle detection with fully convolutional networks"
    - "temporal attention gated model for robust sequence classification"
    - "deeper depth prediction with fully convolutional residual networks"

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.LG(Machine Learning)