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: "mavnet an effective semantic segmentation micro network for mav based tasks"
  Abstract: Real-time semantic image segmentation on platforms subject to size, weight and power (SWaP) constraints is a key area of interest for air surveillance and inspection. In this work, we propose MAVNet: a small, light-weight, deep neural network for real-time semantic segmentation on micro Aerial Vehicles (MAVs). MAVNet, inspired by ERFNet, features 400 times fewer parameters and achieves comparable performance with some reference models in empirical experiments. Our model achieves a trade-off between speed and accuracy, achieving up to 48 FPS on an NVIDIA 1080Ti and 9 FPS on the NVIDIA Jetson Xavier when processing high resolution imagery. Additionally, we provide two novel datasets that represent challenges in semantic segmentation for real-time MAV tracking and infrastructure inspection tasks and verify MAVNet on these datasets. Our algorithm and datasets are made publicly available.

Connected through hyperedge HE-1 (induced by source paper "pst900 rgb thermal calibration dataset and segmentation network"):
  with members:
    - "fast scnn fast semantic segmentation network"
    - "multimodal sensor fusion in single thermal image super resolution"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "understanding convolution for semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "pyramid scene parsing network"
    - "you only look once unified real time object detection"

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"
    - "multi task regression based learning for autonomous unmanned aerial vehicle flight control within unstructured outdoor environments"
    - "visualization and labeling of point clouds in virtual reality"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-3 (induced by source paper "vision based multi mav localization with anonymous relative measurements using coupled probabilistic data association filter"):
  with members:
    - "the open vision computer an integrated sensing and compute system for mobile robots"
    - "fast autonomous flight in gps denied and cluttered environments"
    - "unsupervised deep homography a fast and robust homography estimation model"

Connected through hyperedge HE-4 (induced by source paper "mine tunnel exploration using multiple quadrupedal robots"):
  with members:
    - "the blockchain a new framework for robotic swarm systems"

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)