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: "a unified framework for mutual improvement of slam and semantic segmentation"
  Abstract: This paper presents a novel framework for simultaneously implementing localization and segmentation, which are two of the most important vision-based tasks for robotics. While the goals and techniques used for them were considered to be different previously, we show that by making use of the intermediate results of the two modules, their performance can be enhanced at the same time. Our framework is able to handle both the instantaneous motion and long-term changes of instances in localization with the help of the segmentation result, which also benefits from the refined 3D pose information. We conduct experiments on various datasets, and prove that our framework works effectively on improving the precision and robustness of the two tasks and outperforms existing localization and segmentation algorithms.

Connected through hyperedge HE-1 (induced by source paper "deep direct visual odometry"):
  with members:
    - "selective sensor fusion for neural visual inertial odometry"
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "dsvo direct stereo visual odometry"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "cnn svo improving the mapping in semi direct visual odometry using single image depth prediction"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "direct sparse visual inertial odometry using dynamic marginalization"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"

Connected through hyperedge HE-2 (induced by source paper "movable object aware visual slam via weakly supervised semantic segmentation"):
  with members:
    - "fully using classifiers for weakly supervised semantic segmentation with modified cues"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "ds slam a semantic visual slam towards dynamic environments"
    - "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-3 (induced by source paper "deep learning based wearable assistive system for visually impaired people"):
  with members:
    - "deepvio self supervised deep learning of monocular visual inertial odometry using 3d geometric constraints"
    - "wearable travel aid for environment perception and navigation of visually impaired people"
    - "deep global relative networks for end to end 6 dof visual localization and odometry"
    - "learning transferable uav for forest visual perception"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "smart guiding glasses for visually impaired people in indoor environment"
    - "the cityscapes dataset for semantic urban scene understanding"

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.CV(Computer Vision and Pattern Recognition)