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: "occupancy grids a stochastic spatial representation for active robot perception"
  Abstract: In this paper we provide an overview of a new framework for robot perception, real-world modelling, and navigation that uses a stochastic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field model that maintains probabilistic estimates of the occupancy state of each cell in a spatial lattice. Bayesian estimation mechanisms employing stochastic sensor models allow incremental updating of the Occupancy Grid using multi-view, multi-sensor data, composition of multiple maps, decision-making, and incorporation of robot and sensor position uncertainty. We present the underlying stochastic formulation of the Occupancy Grid framework, and discuss its application to a variety of robotic tusks. These include range-based mapping, multi-sensor integration, path-planning and obstacle avoidance, handling of robot position uncertainty, incorporation of pre-compiled maps, recovery of geometric representations, and other related problems. The experimental results show that the Occupancy Grid approach generates dense world models, is robust under sensor uncertainty and errors, and allows explicit handling of uncertainty. It supports the development of robust and agile sensor interpretation methods, incremental discovery procedures, and composition of information from multiple sources. Furthermore, the results illustrate that robotic tasks can be addressed through operations performed di- rectly on the Occupancy Grid, and that these operations have strong parallels to operations performed in the image processing domain.

Connected through hyperedge HE-1 (induced by source paper "monocular semantic occupancy grid mapping with convolutional variational encoder decoder networks"):
  with members:
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "semantic 3d occupancy mapping through efficient high order crfs"
    - "semantic visual localization"
    - "unsupervised learning of depth and ego motion from video"
    - "cnn slam real time dense monocular slam with learned depth prediction"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-2 (induced by source paper "online active perception for partially observable markov decision processes with limited budget"):
  with members:
    - "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks"
    - "point based pomdp algorithms improved analysis and implementation"

Connected through hyperedge HE-3 (induced by source paper "deep object tracking on dynamic occupancy grid maps using rnns"):
  with members:
    - "offline object extraction from dynamic occupancy grid map sequences"
    - "object detection on dynamic occupancy grid maps using deep learning and automatic label generation"
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "mask r cnn"
    - "fully convolutional neural networks for dynamic object detection in grid maps"
    - "a random finite set approach for dynamic occupancy grid maps with real time application"
    - "ssd single shot multibox detector"
    - "spatially supervised recurrent convolutional neural networks for visual object tracking"

Connected through hyperedge HE-4 (induced by source paper "multi step prediction of occupancy grid maps with recurrent neural networks"):
  with members:
    - "deep object tracking on dynamic occupancy grid maps using rnns"
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "unsupervised learning for physical interaction through video prediction"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "a random finite set approach for dynamic occupancy grid maps with real time application"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "unsupervised learning of video representations using lstms"
    - "spatial transformer networks"

Connected through hyperedge HE-5 (induced by source paper "evidential occupancy grid map augmentation using deep learning"):
  with members:
    - "an analytical lidar sensor model based on ray path information"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "imagenet large scale visual recognition challenge"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "a decentralized mobile computing network for multi robot systems operations"):
  with members:
    - "optimal network topology for responsive collective behavior"
    - "swarm enabling technology for multi robot systems"

Connected through hyperedge HE-7 (induced by source paper "slam with objects using a nonparametric pose graph"):
  with members:
    - "monocular slam supported object recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "robot in a room toward perfect object recognition in closed environments"
    - "imagenet large scale visual recognition challenge"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "revisiting k means new algorithms via bayesian nonparametrics"

Connected through hyperedge HE-8 (induced by source paper "bayes networks for sonar sensor fusion"):
  with members:
    - "sample and accumulate algorithms for belief updating in bayes networks"
    - "sensor validation using dynamic belief networks"
    - "backward simulation in bayesian networks"

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)