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: "enhanced discrete particle swarm optimization path planning for uav vision based surface inspection"
  Abstract: Abstract   In built infrastructure monitoring, an efficient path planning algorithm is essential for robotic inspection of large surfaces using computer vision. In this work, we first formulate the inspection path planning problem as an extended travelling salesman problem (TSP) in which both the coverage and obstacle avoidance were taken into account. An enhanced discrete particle swarm optimization (DPSO) algorithm is then proposed to solve the TSP, with performance improvement by using deterministic initialization, random mutation, and edge exchange. Finally, we take advantage of parallel computing to implement the DPSO in a GPU-based framework so that the computation time can be significantly reduced while keeping the hardware requirement unchanged. To show the effectiveness of the proposed algorithm, experimental results are included for datasets obtained from UAV inspection of an office building and a bridge.

Connected through hyperedge HE-1 (induced by source paper "crack detection using enhanced hierarchical convolutional neural networks"):
  with members:
    - "system architecture for real time surface inspection using multiple uavs"
    - "crack detection using enhanced thresholding on uav based collected images"
    - "tensorflow a system for large scale machine learning"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "system architecture for real time surface inspection using multiple uavs"):
  with members:
    - "angle encoded swarm optimization for uav formation path planning"
    - "adaptive twisting sliding mode control for quadrotor unmanned aerial vehicles"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"

Connected through hyperedge HE-3 (induced by source paper "angle encoded swarm optimization for uav formation path planning"):
  with members:
    - "adaptive twisting sliding mode control for quadrotor unmanned aerial vehicles"
    - "survey of important issues in uav communication networks"

Connected through hyperedge HE-4 (induced by source paper "adaptive twisting sliding mode control for quadrotor unmanned aerial vehicles"):
  with members:
    - "adaptive second order sliding mode control of uavs for civil applications"

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)