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: "maskfusion real time recognition tracking and reconstruction of multiple moving objects"
  Abstract: We present MaskFusion, a real-time, object-aware, semantic and dynamic RGB-D SLAM system that goes beyond traditional systems that output a geometry-only map -- MaskFusion recognizes, segments and assigns semantic class labels to different objects in the scene, while tracking and reconstructing them even when they move independently from the camera. #R##N#As an RGB-D camera scans a cluttered scene, image-based instance-level semantic segmentation creates semantic object masks that enable real-time object recognition and the creation of an object-level representation for the world map. Unlike previous recognition-based SLAM systems, MaskFusion does not require prior knowledge or known models of the objects it can recognize and can deal with multiple independent motions. Unlike recent semantics enabled SLAM systems that perform voxel-level semantic segmentation MaskFusion takes full advantage of using instance-level semantic segmentation to enable semantic labels to be fused into an object-aware map. We show augmented-reality applications, that demonstrate the unique features of the map output by MaskFusion: instance-aware, semantic and dynamic.

Connected through hyperedge HE-1 (induced by source paper "high quality instance aware semantic 3d map using rgb d camera"):
  with members:
    - "fusion volumetric object level slam"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "segicp dsr dense semantic scene reconstruction and registration"
    - "mask r cnn"
    - "meaningful maps with object oriented semantic mapping"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "benchmarking in manipulation research using the yale cmu berkeley object and model set"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "object rpe dense 3d reconstruction and pose estimation with convolutional neural networks for warehouse robots"):
  with members:
    - "high quality instance aware semantic 3d map using rgb d camera"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "fusion volumetric object level slam"
    - "real time seamless single shot 6d object pose prediction"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "mask r cnn"
    - "meaningful maps with object oriented semantic mapping"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "fusion volumetric object level slam"):
  with members:
    - "real time progressive 3d semantic segmentation for indoor scene"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "mask r cnn"
    - "meaningful maps with object oriented semantic mapping"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "monocular slam supported object recognition"

Connected through hyperedge HE-4 (induced by source paper "mid fusion octree based object level multi instance dynamic slam"):
  with members:
    - "fusion volumetric object level slam"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "robust dense mapping for large scale dynamic environments"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"
    - "mask r cnn"
    - "a primer on the differential calculus of 3d orientations"

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)