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: "futuremapping the computational structure of spatial ai systems"
  Abstract: We discuss and predict the evolution of Simultaneous Localisation and Mapping (SLAM) into a general geometric and semantic `Spatial AI' perception capability for intelligent embodied devices. A big gap remains between the visual perception performance that devices such as augmented reality eyewear or comsumer robots will require and what is possible within the constraints imposed by real products. Co-design of algorithms, processors and sensors will be needed. We explore the computational structure of current and future Spatial AI algorithms and consider this within the landscape of ongoing hardware developments.

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"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"

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"
    - "direct sparse odometry"
    - "introducing slambench a performance and accuracy benchmarking methodology for slam"
    - "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 "rlbench the robot learning benchmark learning environment"):
  with members:
    - "pyrep bringing v rep to deep robot learning"
    - "benchmarking simulated robotic manipulation through a real world dataset"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "learning agile and dynamic motor skills for legged robots"
    - "simitate a hybrid imitation learning benchmark"
    - "robonet large scale multi robot learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"

Connected through hyperedge HE-4 (induced by source paper "scenecode monocular dense semantic reconstruction using learned encoded scene representations"):
  with members:
    - "fusion volumetric object level slam"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "da rnn semantic mapping with data associated recurrent neural networks"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-5 (induced by source paper "gradslam dense slam meets automatic differentiation"):
  with members:
    - "end to end lane detection through differentiable least squares fitting"
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "kornia an open source differentiable computer vision library for pytorch"
    - "cubeslam monocular 3 d object slam"
    - "generalized inner loop meta learning"
    - "deeptam deep tracking and mapping"
    - "regnet learning the optimization of direct image to image pose registration"
    - "constructing category specific models for monocular object slam"

Connected through hyperedge HE-6 (induced by source paper "static visual spatial priors for doa estimation"):
  with members:
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "building machines that learn and think like people"
    - "musan a music speech and noise corpus"
    - "conditional random fields as recurrent neural networks"
    - "multi scale context aggregation by dilated convolutions"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-7 (induced by source paper "futuremapping 2 gaussian belief propagation for spatial ai"):
  with members:
    - "visual inertial mapping with non linear factor recovery"
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "geometric deep learning going beyond euclidean data"
    - "distributed mapping with privacy and communication constraints lightweight algorithms and object based models"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "self improving visual odometry"):
  with members:
    - "lf net learning local features from images"
    - "sips succinct interest points from unsupervised inlierness probability learning"
    - "sips unsupervised succinct interest points"
    - "megadepth learning single view depth prediction from internet photos"
    - "matching features without descriptors implicitly matched interest points imips"
    - "superpoint self supervised interest point detection and description"
    - "convolutional neural network architecture for geometric matching"
    - "unsupervised learning of depth and ego motion from video"

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.AI(Artificial Intelligence)