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 "incremental class discovery for semantic segmentation with rgbd sensing"):
  Abstract: This work addresses the task of open world semantic segmentation using RGBD sensing to discover new semantic classes over time. Although there are many types of objects in the real-word, current semantic segmentation methods make a closed world assumption and are trained only to segment a limited number of object classes. Towards a more open world approach, we propose a novel method that incrementally learns new classes for image segmentation. The proposed system first segments each RGBD frame using both color and geometric information, and then aggregates that information to build a single segmented dense 3D map of the environment. The segmented 3D map representation is a key component of our approach as it is used to discover new object classes by identifying coherent regions in the 3D map that have no semantic label. The use of coherent region in the 3D map as a primitive element, rather than traditional elements such as surfels or voxels, also significantly reduces the computational complexity and memory use of our method. It thus leads to semi-real-time performance at {10.7}Hz when incrementally updating the dense 3D map at every frame. Through experiments on the NYUDv2 dataset, we demonstrate that the proposed method is able to correctly cluster objects of both known and unseen classes. We also show the quantitative comparison with the state-of-the-art supervised methods, the processing time of each step, and the influences of each component.

Includes members:
  Member "fast and accurate semantic mapping through geometric based incremental segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "volumetric instance aware semantic mapping and 3d object discovery")
  Member "sgpn similarity group proposal network for 3d point cloud instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "review deep learning on 3d point clouds")
  Member "infinitam v3 a framework for large scale 3d reconstruction with loop closure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation")
  Member "semantic 3d occupancy mapping through efficient high order crfs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "occuseg occupancy aware 3d instance segmentation")
  Member "segcloud semantic segmentation of 3d point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "occuseg occupancy aware 3d instance segmentation")
  Member "pointwise convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review deep learning on 3d point clouds")
  Member "w net a deep model for fully unsupervised image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mumford shah loss functional for image segmentation with deep learning")
  Member "multiscale combinatorial grouping for image segmentation and object proposal generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")

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)