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: "constructing category specific models for monocular object slam"
  Abstract: We present a new paradigm for real-time object-oriented SLAM with a monocular camera. Contrary to previous approaches, that rely on object-level models, we construct category-level models from CAD collections which are now widely available. To alleviate the need for huge amounts of labeled data, we develop a rendering pipeline that enables synthesis of large datasets from a limited amount of manually labeled data. Using data thus synthesized, we learn category-level models for object deformations in 3D, as well as discriminative object features in 2D. These category models are instance-independent and aid in the design of object landmark observations that can be incorporated into a generic monocular SLAM framework. Where typical object-SLAM approaches usually solve only for object and camera poses, we also estimate object shape on-the-fly, allowing for a wide range of objects from the category to be present in the scene. Moreover, since our 2D object features are learned discriminatively, the proposed object-SLAM system succeeds in several scenarios where sparse feature-based monocular SLAM fails due to insufficient features or parallax. Also, the proposed category-models help in object instance retrieval, useful for Augmented Reality (AR) applications. We evaluate the proposed framework on multiple challenging real-world scenes and show --- to the best of our knowledge --- first results of an instance-independent monocular object-SLAM system and the benefits it enjoys over feature-based SLAM methods.

Connected through hyperedge HE-1 (induced by source paper "not only look but observe variational observation model of scene level 3d multi object understanding for probabilistic slam"):
  with members:
    - "toward predictive machine learning for active vision"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "a variational feature encoding method of 3d object for probabilistic semantic slam"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "focal loss for dense object detection"
    - "image2mesh a learning framework for single image 3d reconstruction"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"

Connected through hyperedge HE-2 (induced by source paper "gradslam dense slam meets automatic differentiation"):
  with members:
    - "end to end lane detection through differentiable least squares fitting"
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "kornia an open source differentiable computer vision library for pytorch"
    - "cubeslam monocular 3 d object slam"
    - "generalized inner loop meta learning"
    - "deeptam deep tracking and mapping"
    - "regnet learning the optimization of direct image to image pose registration"
    - "codeslam learning a compact optimisable representation for dense visual slam"

Connected through hyperedge HE-3 (induced by source paper "integrating objects into monocular slam line based category specific models"):
  with members:
    - "cubeslam monocular 3d object detection and slam without prior models"
    - "edge slam edge points based monocular visual slam"
    - "slam with objects using a nonparametric pose graph"
    - "object centric photometric bundle adjustment with deep shape prior"
    - "stacked hourglass networks for human pose estimation"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "shapenet an information rich 3d model repository"

Connected through hyperedge HE-4 (induced by source paper "a variational observation model of 3d object for probabilistic semantic slam"):
  with members:
    - "a variational feature encoding method of 3d object for probabilistic semantic slam"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "a survey of structure from motion"
    - "yolo9000 better faster stronger"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "the earth ain t flat monocular reconstruction of vehicles on steep and graded roads from a moving camera"):
  with members:
    - "slanted stixels representing san francisco s steepest streets"
    - "accurate single stage detector using recurrent rolling convolution"
    - "stacked hourglass networks for human pose estimation"
    - "reconstructing vechicles from a single image shape priors for road scene understanding"
    - "sparseness meets deepness 3d human pose estimation from monocular video"
    - "shapenet an information rich 3d model repository"
    - "towards scene understanding with detailed 3d object representations"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"

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)