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: "introducing slambench a performance and accuracy benchmarking methodology for slam"
  Abstract: Real-time dense computer vision and SLAM offer great potential for a new level of scene modelling, tracking and real environmental interaction for many types of robot, but their high computational requirements mean that use on mass market embedded platforms is challenging. Meanwhile, trends in low-cost, low-power processing are towards massive parallelism and heterogeneity, making it difficult for robotics and vision researchers to implement their algorithms in a performance-portable way. In this paper we introduce SLAMBench, a publicly-available software framework which represents a starting point for quantitative, comparable and validatable experimental research to investigate trade-offs in performance, accuracy and energy consumption of a dense RGB-D SLAM system. SLAMBench provides a KinectFusion implementation in C++, OpenMP, OpenCL and CUDA, and harnesses the ICL-NUIM dataset of synthetic RGB-D sequences with trajectory and scene ground truth for reliable accuracy comparison of different implementation and algorithms. We present an analysis and breakdown of the constituent algorithmic elements of KinectFusion, and experimentally investigate their execution time on a variety of multicore and GPU-accelerated platforms. For a popular embedded platform, we also present an analysis of energy efficiency for different configuration alternatives.

Connected through hyperedge HE-1 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency"):
  with members:
    - "low latency visual slam with appearance enhanced local map building"
    - "good feature selection for least squares pose optimization in vo vslam"
    - "navigating the landscape for real time localization and mapping for robotics and virtual and augmented reality"
    - "the open vision computer an integrated sensing and compute system for mobile robots"
    - "direct sparse odometry with rolling shutter"
    - "futuremapping the computational structure of spatial ai systems"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "the tum vi benchmark for evaluating visual inertial odometry"

Connected through hyperedge HE-2 (induced by source paper "bundle adjustment on a graph processor"):
  with members:
    - "futuremapping 2 gaussian belief propagation for spatial ai"
    - "dissecting the graphcore ipu architecture via microbenchmarking"
    - "navigating the landscape for real time localization and mapping for robotics and virtual and augmented reality"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "futuremapping the computational structure of spatial ai systems"
    - "direct sparse odometry"
    - "orb slam a versatile and accurate monocular slam system"
    - "loopy belief propagation for approximate inference an empirical study"

Connected through hyperedge HE-3 (induced by source paper "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"):
  with members:
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "mavbench micro aerial vehicle benchmarking"
    - "deep reinforcement learning for swarm systems"
    - "deep drone racing learning agile flight in dynamic environments"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning for uav attitude control"
    - "learning navigation behaviors end to end with autorl"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-4 (induced by source paper "gslam a general slam framework and benchmark"):
  with members:
    - "direct sparse visual inertial odometry using dynamic marginalization"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "ldso direct sparse odometry with loop closure"
    - "high quality monocular depth estimation via transfer learning"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "topometric localization with deep learning"

Connected through hyperedge HE-5 (induced by source paper "characterizing slam benchmarks and methods for the robust perception age"):
  with members:
    - "good feature matching towards accurate robust vo vslam with low latency"
    - "low latency visual slam with appearance enhanced local map building"
    - "good feature selection for least squares pose optimization in vo vslam"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "the blackbird dataset a large scale dataset for uav perception in aggressive flight"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "direct sparse odometry"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-6 (induced by source paper "the role of compute in autonomous aerial vehicles"):
  with members:
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "autonomous recharging and flight mission planning for battery operated autonomous drones"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "unrealcv connecting computer vision to unreal engine"
    - "yolo9000 better faster stronger"
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-7 (induced by source paper "mavbench micro aerial vehicle benchmarking"):
  with members:
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "unrealcv connecting computer vision to unreal engine"
    - "yolo9000 better faster stronger"
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-8 (induced by source paper "futuremapping the computational structure of spatial ai systems"):
  with members:
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "unsupervised learning of depth and ego motion from video"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "algorithmic performance accuracy trade off in 3d vision applications using hypermapper"
    - "da rnn semantic mapping with data associated recurrent neural 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)