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: "sampling based incremental information gathering with applications to robotic exploration and environmental monitoring"
  Abstract: We propose a sampling-based motion-planning algorithm equipped with an information-theoretic convergence criterion for incremental informative motion planning. The proposed approach allows dense ma...

Connected through hyperedge HE-1 (induced by source paper "active learning for uav based semantic mapping"):
  with members:
    - "detecting the unexpected via image resynthesis"
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "modular sensor fusion for semantic segmentation"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning"
    - "cost effective active learning for deep image classification"
    - "distance based confidence score for neural network classifiers"

Connected through hyperedge HE-2 (induced by source paper "bayesian spatial kernel smoothing for scalabledense semantic mapping"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "rtseg real time semantic segmentation comparative study"
    - "contact aided invariant extended kalman filtering for legged robot state estimation"
    - "semantic 3d occupancy mapping through efficient high order crfs"
    - "gaussian processes semantic map representation"
    - "sparse bayesian inference for dense semantic mapping"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "probe gk predictive robust estimation using generalized kernels"

Connected through hyperedge HE-3 (induced by source paper "informative path planning and mapping for active sensing under localization uncertainty"):
  with members:
    - "bayesian optimisation for safe navigation under localisation uncertainty"
    - "an informative path planning framework for uav based terrain monitoring"
    - "multiresolution mapping and informative path planning for uav based terrain monitoring"

Connected through hyperedge HE-4 (induced by source paper "informative path planning for active field mapping under localization uncertainty"):
  with members:
    - "bayesian optimisation for safe navigation under localisation uncertainty"
    - "an informative path planning framework for uav based terrain monitoring"
    - "multiresolution mapping and informative path planning for uav based terrain monitoring"

Connected through hyperedge HE-5 (induced by source paper "an informative path planning framework for uav based terrain monitoring"):
  with members:
    - "hindsight is only 50 50 unsuitability of mdp based approximate pomdp solvers for multi resolution information gathering"
    - "a data driven approach for autonomous motion planning and control in off road driving scenarios"
    - "algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning"
    - "multiresolution mapping and informative path planning for uav based terrain monitoring"
    - "dynamic system identification and control for a cost effective open source vtol mav"
    - "weednet dense semantic weed classification using multispectral images and mav for smart farming"
    - "modeling curiosity in a mobile robot for long term autonomous exploration and monitoring"
    - "pomdp lite for robust robot planning under uncertainty"

Connected through hyperedge HE-6 (induced by source paper "kriging based robotic exploration for soil moisture mapping using a cosmic ray sensor"):
  with members:
    - "multiresolution mapping and informative path planning for uav based terrain monitoring"

Connected through hyperedge HE-7 (induced by source paper "gaussian processes semantic map representation"):
  with members:
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "monocular slam supported object recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "learning deconvolution network for semantic segmentation"
    - "fully convolutional networks for semantic segmentation"

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)