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: "monocular slam supported object recognition"
  Abstract: In this work, we develop a monocular SLAM-aware object recognition system that is able to achieve considerably stronger recognition performance, as compared to classical object recognition systems that function on a frame-by-frame basis. By incorporating several key ideas including multi-view object proposals and efficient feature encoding methods, our proposed system is able to detect and robustly recognize objects in its environment using a single RGB camera in near-constant time. Through experiments, we illustrate the utility of using such a system to effectively detect and recognize objects, incorporating multiple object viewpoint detections into a unified prediction hypothesis. The performance of the proposed recognition system is evaluated on the UW RGB-D Dataset, showing strong recognition performance and scalable run-time performance compared to current state-of-the-art recognition systems.

Connected through hyperedge HE-1 (induced by source paper "alpha n shortest path finder automated delivery robot with obstacle detection and avoiding system"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "you only look once unified real time object detection"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "df slam a deep learning enhanced visual slam system based on deep local features"):
  with members:
    - "lf net learning local features from images"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "local descriptors optimized for average precision"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "superpoint self supervised interest point detection and description"
    - "repeatability is not enough learning affine regions via discriminability"
    - "unsupervised learning of depth and ego motion from video"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"

Connected through hyperedge HE-3 (induced by source paper "simvodis simultaneous visual odometry object detection and instance segmentation"):
  with members:
    - "3 d scene graph a sparse and semantic representation of physical environments for intelligent agents"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "fusion volumetric object level slam"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "ds slam a semantic visual slam towards dynamic environments"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "digging into self supervised monocular depth estimation"

Connected through hyperedge HE-4 (induced by source paper "the limits and potentials of deep learning for robotics"):
  with members:
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "on calibration of modern neural networks"
    - "domain adaptation for visual applications a comprehensive survey"
    - "progressive neural networks"
    - "core50 a new dataset and benchmark for continuous object recognition"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "sparse representation based open set recognition"
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"

Connected through hyperedge HE-5 (induced by source paper "physics based scene level reasoning for object pose estimation in clutter"):
  with members:
    - "fusion volumetric object level slam"
    - "bop benchmark for 6d object pose estimation"
    - "robust 6d object pose estimation with stochastic congruent sets"
    - "6d pose estimation using an improved method based on point pair features"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "rotational subgroup voting and pose clustering for robust 3d object recognition"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "genesis rt generating synthetic images for training secondary real world tasks"

Connected through hyperedge HE-6 (induced by source paper "a unified framework for multi view multi class object pose estimation"):
  with members:
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "real time seamless single shot 6d object pose prediction"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "cnn slam real time dense monocular slam with learned depth prediction"
    - "mask r cnn"
    - "3d bounding box estimation using deep learning and geometry"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"

Connected through hyperedge HE-7 (induced by source paper "monocular object and plane slam in structured environments"):
  with members:
    - "cubeslam monocular 3 d object slam"
    - "fusion volumetric object level slam"
    - "structure aware slam using quadrics and planes"
    - "cubeslam monocular 3d object detection and slam without prior models"
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"
    - "cooperative holistic scene understanding unifying 3d object layout and camera pose estimation"
    - "joint layout estimation and global multi view registration for indoor reconstruction"
    - "semantic 3d occupancy mapping through efficient high order crfs"

Connected through hyperedge HE-8 (induced by source paper "semantic mapping with simultaneous object detection and localization"):
  with members:
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"
    - "multiple object detection tracking and long term dynamics learning in large 3d maps"
    - "real time seamless single shot 6d object pose prediction"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "da rnn semantic mapping with data associated recurrent neural networks"
    - "semantic robot programming for goal directed manipulation in cluttered scenes"
    - "meaningful maps with object oriented semantic mapping"
    - "ssd single shot multibox detector"

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)