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: "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 .

Connected through hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans"):
  with members:
    - "teaser fast and certifiable point cloud registration"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "panoptic segmentation"
    - "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"
    - "semi supervised semantic mapping through label propagation with semantic texture meshes"
    - "kimera an open source library for real time metric semantic localization and mapping"
    - "3 d scene graph a sparse and semantic representation of physical environments for intelligent agents"
    - "value iteration networks on multiple levels of abstraction"

Connected through hyperedge HE-2 (induced by source paper "comparing view based and map based semantic labelling in real time slam"):
  with members:
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "orthographic feature transform for monocular 3d object detection"
    - "octnetfusion learning depth fusion from data"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "da rnn semantic mapping with data associated recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "kimera an open source library for real time metric semantic localization and mapping"):
  with members:
    - "interlaced sparse self attention for semantic segmentation"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "dual graph convolutional network for semantic segmentation"
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "incremental visual inertial 3d mesh generation with structural regularities"
    - "active scene understanding via online semantic reconstruction"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "segmap 3d segment mapping using data driven descriptors"

Connected through hyperedge HE-4 (induced by source paper "casting geometric constraints in semantic segmentation as semi supervised learning"):
  with members:
    - "open3d a modern library for 3d data processing"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "digging into self supervised monocular depth estimation"
    - "good semi supervised learning that requires a bad gan"

Connected through hyperedge HE-5 (induced by source paper "learning densities in feature space for reliable segmentation of indoor scenes"):
  with members:
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "fusion volumetric object level slam"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "mid fusion octree based object level multi instance dynamic slam"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"

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)