human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "analyzing modular cnn architectures for joint depth prediction and semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "um adapt unsupervised multi task adaptation using adversarial cross task distillation")
  Member "obstacle avoidance through deep networks based intermediate perception":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "danger aware adaptive composition of drl agents for self navigation")
  Member "airsim high fidelity visual and physical simulation for autonomous vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "learning depth from single monocular images using deep convolutional neural fields":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "3d bounding box estimation using deep learning and geometry":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "robust monocular flight in cluttered outdoor environments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gonet a semi supervised deep learning approach for traversability estimation")
  Member "you only look once unified real time object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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)