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: "a fully end to end deep learning approach for real time simultaneous 3d reconstruction and material recognition"
  Abstract: This paper addresses the problem of simultaneous 3D reconstruction and material recognition and segmentation. Enabling robots to recognise different materials (concrete, metal etc.) in a scene is important for many tasks, e.g. robotic interventions in nuclear decommissioning. Previous work on 3D semantic reconstruction has predominantly focused on recognition of everyday domestic objects (tables, chairs etc.), whereas previous work on material recognition has largely been confined to single 2D images without any 3D reconstruction. Meanwhile, most 3D semantic reconstruction methods rely on computationally expensive post-processing, using Fully-Connected Conditional Random Fields (CRFs), to achieve consistent segmentations. In contrast, we propose a deep learning method which performs 3D reconstruction while simultaneously recognising different types of materials and labeling them at the pixel level. Unlike previous methods, we propose a fully end-to-end approach, which does not require hand-crafted features or CRF post-processing. Instead, we use only learned features, and the CRF segmentation constraints are incorporated inside the fully end-to-end learned system. We present the results of experiments, in which we trained our system to perform real-time 3D semantic reconstruction for 23 different materials in a real-world application. The run-time performance of the system can be boosted to around 10Hz, using a conventional GPU, which is enough to achieve realtime semantic reconstruction using a 30fps RGB-D camera. To the best of our knowledge, this work is the first real-time end-to-end system for simultaneous 3D reconstruction and material recognition.

Connected through hyperedge HE-1 (induced by source paper "direct visual inertial odometry with semi dense mapping"):
  with members:
    - "stacked wasserstein autoencoder"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "on manifold preintegration for real time visual inertial odometry"
    - "cnn slam real time dense monocular slam with learned depth prediction"
    - "direct sparse odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-2 (induced by source paper "epaner team description paper for world robot challenge 2020"):
  with members:
    - "human motion trajectory prediction a survey"
    - "multisensor online transfer learning for 3d lidar based human classification with a mobile robot"
    - "3dof pedestrian trajectory prediction learned from long term autonomous mobile robot deployment data"
    - "robots that can adapt like animals"

Connected through hyperedge HE-3 (induced by source paper "learning monocular visual odometry with dense 3d mapping from dense 3d flow"):
  with members:
    - "ls vo learning dense optical subspace for robust visual odometry estimation"
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "unsupervised learning of depth and ego motion from video"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "demon depth and motion network for learning monocular stereo"
    - "undeepvo monocular visual odometry through unsupervised deep learning"

Connected through hyperedge HE-4 (induced by source paper "recurrent octomap learning state based map refinement for long term semantic mapping with 3d lidar data"):
  with members:
    - "dense rgb d semantic mapping with pixel voxel neural network"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "exploring spatial context for 3d semantic segmentation of point clouds"
    - "3dof pedestrian trajectory prediction learned from long term autonomous mobile robot deployment data"
    - "da rnn semantic mapping with data associated recurrent neural networks"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"
    - "3d fully convolutional network for vehicle detection in point cloud"

Connected through hyperedge HE-5 (induced by source paper "context aware mixed reality a framework for ubiquitous interaction"):
  with members:
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "cnn slam real time dense monocular slam with learned depth prediction"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "orb slam a versatile and accurate monocular slam system"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "dense rgb d semantic mapping with pixel voxel neural network"):
  with members:
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "da rnn semantic mapping with data associated recurrent neural networks"
    - "multi view deep learning for consistent semantic mapping with rgb d cameras"
    - "cnn slam real time dense monocular slam with learned depth prediction"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "exploring context with deep structured models for semantic segmentation"

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)