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: "ds slam a semantic visual slam towards dynamic environments"
  Abstract: Simultaneous Localization and Mapping (SLAM) is considered to be a fundamental capability for intelligent mobile robots. Over the past decades, many impressed SLAM systems have been developed and achieved good performance under certain circumstances. However, some problems are still not well solved, for example, how to tackle the moving objects in the dynamic environments, how to make the robots truly understand the surroundings and accomplish advanced tasks. In this paper, a robust semantic visual SLAM towards dynamic environments named DS-SLAM is proposed. Five threads run in parallel in DS-SLAM: tracking, semantic segmentation, local mapping, loop closing and dense semantic map creation. DS-SLAM combines semantic segmentation network with moving consistency check method to reduce the impact of dynamic objects, and thus the localization accuracy is highly improved in dynamic environments. Meanwhile, a dense semantic octo-tree map is produced, which could be employed for high-level tasks. We conduct experiments both on TUM RGB-D dataset and in real-world environment. The results demonstrate the absolute trajectory accuracy in DS-SLAM can be improved one order of magnitude compared with ORB-SLAM2. It is one of the state-of-the-art SLAM systems in high-dynamic environments.

Connected through hyperedge HE-1 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview"):
  with members:
    - "selective sensor fusion for neural visual inertial odometry"
    - "learning monocular depth estimation infusing traditional stereo knowledge"
    - "deep direct visual odometry"
    - "fastdepth fast monocular depth estimation on embedded systems"
    - "recurrent neural network for un supervised learning of monocular videovisual odometry and depth"
    - "multi frame gan image enhancement for stereo visual odometry in low light"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "sganvo unsupervised deep visual odometry and depth estimation with stacked generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "dymslam 4d dynamic scene reconstruction based on geometrical motion segmentation"):
  with members:
    - "track to reconstruct and reconstruct to track"
    - "stereo based multi motion visual odometry for mobile robots"
    - "unsupervised monocular depth and ego motion learning with structure and semantics"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "multimotion visual odometry mvo simultaneous estimation of camera and third party motions"
    - "mid fusion octree based object level multi instance dynamic slam"

Connected through hyperedge HE-3 (induced by source paper "flowfusion dynamic dense rgb d slam based on optical flow"):
  with members:
    - "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"
    - "mid fusion octree based object level multi instance dynamic slam"
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "optical flow in mostly rigid scenes"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "learning to refine object segments"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-4 (induced by source paper "simvodis simultaneous visual odometry object detection and instance segmentation"):
  with members:
    - "3 d scene graph a sparse and semantic representation of physical environments for intelligent agents"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "fusion volumetric object level slam"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "digging into self supervised monocular depth estimation"
    - "depth prediction without the sensors leveraging structure for unsupervised learning from monocular videos"

Connected through hyperedge HE-5 (induced by source paper "movable object aware visual slam via weakly supervised semantic segmentation"):
  with members:
    - "a unified framework for mutual improvement of slam and semantic segmentation"
    - "fully using classifiers for weakly supervised semantic segmentation with modified cues"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "icnet for real time semantic segmentation on high resolution images"
    - "keyframe based monocular slam"
    - "mask r cnn"

Connected through hyperedge HE-6 (induced by source paper "semantic nearest neighbor fields monocular edge visual odometry"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "simultaneous edge alignment and learning"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "casenet deep category aware semantic edge detection"
    - "semi dense visual odometry for rgb d cameras using approximate nearest neighbour fields"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "direct sparse odometry"

Connected through hyperedge HE-7 (induced by source paper "are we ready for service robots the openloris scene datasets for lifelong slam"):
  with members:
    - "openloris object a robotic vision dataset and benchmark for lifelong deep learning"
    - "gslam a general slam framework and benchmark"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "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"
    - "scenenet rgb d 5m photorealistic images of synthetic indoor trajectories with ground truth"
    - "direct sparse odometry"

Connected through hyperedge HE-8 (induced by source paper "gmc grid based motion clustering in dynamic environment"):
  with members:
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "visual slam with network uncertainty informed feature selection"
    - "semantic visual localization"
    - "mask r cnn"
    - "you only look once unified real time object detection"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "faster r cnn towards real time object detection with region proposal 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)