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: "fusion volumetric object level slam"
  Abstract: We propose an online object-level SLAM system which builds a persistent and accurate 3D graph map of arbitrary reconstructed objects. As an RGB-D camera browses a cluttered indoor scene, Mask-RCNN instance segmentations are used to initialise compact per-object Truncated Signed Distance Function (TSDF) reconstructions with object size-dependent resolutions and a novel 3D foreground mask. Reconstructed objects are stored in an optimisable 6DoF pose graph which is our only persistent map representation. Objects are incrementally refined via depth fusion, and are used for tracking, relocalisation and loop closure detection. Loop closures cause adjustments in the relative pose estimates of object instances, but no intra-object warping. Each object also carries semantic information which is refined over time and an existence probability to account for spurious instance predictions. We demonstrate our approach on a hand-held RGB-D sequence from a cluttered office scene with a large number and variety of object instances, highlighting how the system closes loops and makes good use of existing objects on repeated loops. We quantitatively evaluate the trajectory error of our system against a baseline approach on the RGB-D SLAM benchmark, and qualitatively compare reconstruction quality of discovered objects on the YCB video dataset. Performance evaluation shows our approach is highly memory efficient and runs online at 4-8Hz (excluding relocalisation) despite not being optimised at the software level.

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 "towards bounding box free panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "panoptic edge detection"
    - "upsnet a unified panoptic segmentation network"
    - "fast panoptic segmentation network"
    - "adaptis adaptive instance selection network"
    - "ssap single shot instance segmentation with affinity pyramid"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"

Connected through hyperedge HE-3 (induced by source paper "globally optimal point set registration by joint symmetry plane fitting"):
  with members:
    - "shapenet an information rich 3d model repository"
    - "point set registration coherent point drift"

Connected through hyperedge HE-4 (induced by source paper "rlbench the robot learning benchmark learning environment"):
  with members:
    - "pyrep bringing v rep to deep robot learning"
    - "benchmarking simulated robotic manipulation through a real world dataset"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "learning agile and dynamic motor skills for legged robots"
    - "simitate a hybrid imitation learning benchmark"
    - "robonet large scale multi robot learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"

Connected through hyperedge HE-5 (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"
    - "volumetric instance aware semantic mapping and 3d object discovery"
    - "active scene understanding via online semantic reconstruction"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"

Connected through hyperedge HE-6 (induced by source paper "where are the keys learning object centric navigation policies on semantic maps with graph convolutional networks"):
  with members:
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "graph policy gradients for large scale robot control"
    - "representation learning on graphs a reinforcement learning application"
    - "learning heuristics over large graphs via deep reinforcement learning"
    - "deep slam object level rgbd slam based on class specific deep shape priors"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"

Connected through hyperedge HE-7 (induced by source paper "the fishyscapes benchmark measuring blind spots in semantic segmentation"):
  with members:
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "semantic foggy scene understanding with synthetic data"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "evaluating bayesian deep learning methods for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "x section cross section prediction for enhanced rgbd fusion"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "learning to reconstruct shapes from unseen classes"
    - "octnetfusion learning depth fusion from data"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "weakly supervised 3d reconstruction with adversarial constraint"

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)