human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "rescan inductive instance segmentation for indoor rgbd scans"):
  Abstract: In depth-sensing applications ranging from home robotics to AR/VR, it will be common to acquire 3D scans of interior spaces repeatedly at sparse time intervals (e.g., as part of regular daily use). We propose an algorithm that analyzes these "rescans" to infer a temporal model of a scene with semantic instance information. Our algorithm operates inductively by using the temporal model resulting from past observations to infer an instance segmentation of a new scan, which is then used to update the temporal model. The model contains object instance associations across time and thus can be used to track individual objects, even though there are only sparse observations. During experiments with a new benchmark for the new task, our algorithm outperforms alternate approaches based on state-of-the-art networks for semantic instance segmentation.

Includes members:
  Member "constructaide analyzing and visualizing construction sites through photographs and building models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploiting 2d floorplan for building scale panorama rgbd alignment")
  Member "masc multi scale affinity with sparse convolution for 3d instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "occuseg occupancy aware 3d instance segmentation")
  Member "detection and tracking of general movable objects in large 3d maps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multiple object detection tracking and long term dynamics learning in large 3d maps")
  Member "3d semantic segmentation with submanifold sparse convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pointasnl robust point clouds processing using nonlocal neural networks with adaptive sampling")
  Member "matterport3d learning from rgb d data in indoor environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an exploration of embodied visual exploration")
  Member "the strands project long term autonomy in everyday environments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "designing interaction for multi agent cooperative system in an office environment")
  Member "learning hierarchical object maps of non stationary environments with mobile robots":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "detection and tracking of general movable objects in large 3d maps")

Question: Please tell me which class the source paper 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)