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: "contextually guided semantic labeling and search for 3d point clouds"
  Abstract: RGB-D cameras, which give an RGB image to- gether with depths, are becoming increasingly popular for robotic perception. In this paper, we address the task of detecting commonly found objects in the 3D point cloud of indoor scenes obtained from such cameras. Our method uses a graphical model that captures various features and contextual relations, including the local visual appearance and shape cues, object co-occurence relationships and geometric relationships. With a large number of object classes and relations, the model's parsimony becomes important and we address that by using multiple types of edge potentials. We train the model using a maximum-margin learning approach. In our experiments over a total of 52 3D scenes of homes and offices (composed from about 550 views), we get a performance of 84.06% and 73.38% in labeling office and home scenes respectively for 17 object classes each. We also present a method for a robot to search for an object using the learned model and the contextual information available from the current labelings of the scene. We applied this algorithm successfully on a mobile robot for the task of finding 12 object classes in 10 different offices and achieved a precision of 97.56% with 78.43% recall.

Connected through hyperedge HE-1 (induced by source paper "segcloud semantic segmentation of 3d point clouds"):
  with members:
    - "deep projective 3d semantic segmentation"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "scannet richly annotated 3d reconstructions of indoor scenes"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-2 (induced by source paper "what is missing or wrong in the scene a hybrid deep boltzmann machine for contextualized scene modeling"):
  with members:
    - "context reasoning in underwater robots using mebn"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "robobrain large scale knowledge engine for robots"
    - "towards a domain specific language for a scene graph based robotic world model"

Connected through hyperedge HE-3 (induced by source paper "fine to coarse global registration of rgb d scans"):
  with members:
    - "de noising stabilizing and completing 3d reconstructions on the go using plane priors"
    - "3dmatch learning local geometric descriptors from rgb d reconstructions"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "scenenet understanding real world indoor scenes with synthetic data"
    - "convolutional neural network based place recognition"

Connected through hyperedge HE-4 (induced by source paper "robobrain large scale knowledge engine for robots"):
  with members:
    - "planit a crowdsourcing approach for learning to plan paths from large scale preference feedback"
    - "learning rich features from rgb d images for object detection and segmentation"
    - "deep learning for detecting robotic grasps"
    - "learning trajectory preferences for manipulators via iterative improvement"
    - "learning object arrangements in 3d scenes using human context"

Connected through hyperedge HE-5 (induced by source paper "learning human activities and object affordances from rgb d videos"):
  with members:
    - "learning object arrangements in 3d scenes using human context"
    - "human activity learning using object affordances from rgb d videos"
    - "learning to place new objects in a scene"
    - "unstructured human activity detection from rgbd images"

Connected through hyperedge HE-6 (induced by source paper "3d scene grammar for parsing rgb d pointclouds"):
  with members:
    - "unstructured human activity detection from rgbd images"
    - "the generalized a architecture"

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)