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 "volumetric instance aware semantic mapping and 3d object discovery"):
  Abstract: To autonomously navigate and plan interactions in real-world environments, robots require the ability to robustly perceive and map complex, unstructured surrounding scenes. Besides building an internal representation of the observed scene geometry, the key insight toward a truly functional understanding of the environment is the usage of higher level entities during mapping, such as individual object instances. This work presents an approach to incrementally build volumetric object-centric maps during online scanning with a localized RGB-D camera. First, a per-frame segmentation scheme combines an unsupervised geometric approach with instance-aware semantic predictions to detect both recognized scene elements as well as previously unseen objects. Next, a data association step tracks the predicted instances across the different frames. Finally, a map integration strategy fuses information about their 3D shape, location, and, if available, semantic class into a global volume. Evaluation on a publicly available dataset shows that the proposed approach for building instance-level semantic maps is competitive with state-of-the-art methods, while additionally able to discover objects of unseen categories. The system is further evaluated within a real-world robotic mapping setup, for which qualitative results highlight the online nature of the method. Code is available at  https://github.com/ethz-asl/voxblox-plusplus .

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 "incremental class discovery for semantic segmentation with rgbd sensing")
  Member "fusion volumetric object level slam":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "maplab an open framework for research in visual inertial mapping and localization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "whole body control of a mobile manipulator using end to end reinforcement learning")
  Member "real time progressive 3d semantic segmentation for indoor scene":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fusion aware point convolution for online semantic 3d scene segmentation")
  Member "incremental object database building 3d models from multiple partial observations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep slam object level rgbd slam based on class specific deep shape priors")
  Member "scenecut joint geometric and object segmentation for indoor scenes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mumford shah loss functional for image segmentation with deep learning")
  Member "infinitam v3 a framework for large scale 3d reconstruction with loop closure":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "let s take this online adapting scene coordinate regression network predictions for online rgb d camera relocalisation")
  Member "learning to segment every thing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")

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.RO(Robotics)