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: "multi task regression based learning for autonomous unmanned aerial vehicle flight control within unstructured outdoor environments"
  Abstract: Increased growth in the global Unmanned Aerial Vehicles (UAV) (drone) industry has expanded possibilities for fully autonomous UAV applications. A particular application which has in part motivated this research is the use of UAV in wide area search and surveillance operations in unstructured outdoor environments. The critical issue with such environments is the lack of structured features that could aid in autonomous flight, such as road lines or paths. In this paper, we propose an End-to-End Multi-Task Regression-based Learning approach capable of defining flight commands for navigation and exploration under the forest canopy, regardless of the presence of trails or additional sensors (i.e. GPS). Training and testing are performed using a software in the loop pipeline which allows for a detailed evaluation against state-of-the-art pose estimation techniques. Our extensive experiments demonstrate that our approach excels in performing dense exploration within the required search perimeter, is capable of covering wider search regions, generalises to previously unseen and unexplored environments and outperforms contemporary state-of-the-art techniques.

Connected through hyperedge HE-1 (induced by source paper "online deep reinforcement learning for autonomous uav navigation and exploration of outdoor environments"):
  with members:
    - "visual appearance analysis of forest scenes for monocular slam"
    - "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"
    - "transfer and online reinforcement learning in stt mram based embedded systems for autonomous drones"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "rainbow combining improvements in deep reinforcement learning"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "learning what data to learn"

Connected through hyperedge HE-2 (induced by source paper "sloam semantic lidar odometry and mapping for forest inventory"):
  with members:
    - "automatic 3d mapping for tree diameter measurements in inventory operations"
    - "monocular camera based fruit counting and mapping with semantic data association"
    - "mavnet an effective semantic segmentation micro network for mav based tasks"
    - "visualization and labeling of point clouds in virtual reality"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

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.RO(Robotics)