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: "building an integrated mobile robotic system for real time applications in construction"
  Abstract: One of the major challenges of a real-time autonomous robotic system for construction monitoring is to simultaneously localize, map, and navigate over the lifetime of the robot, with little or no human intervention. Past research on Simultaneous Localization and Mapping (SLAM) and context-awareness are two active research areas in the computer vision and robotics communities. The studies that integrate both in real-time into a single modular framework for construction monitoring still need further investigation. A monocular vision system and real-time scene understanding are computationally heavy and the major state-of-the-art algorithms are tested on high-end desktops and/or servers with a high CPU- and/or GPU- computing capabilities, which affect their mobility and deployment for real-world applications. To address these challenges and achieve automation, this paper proposes an integrated robotic computer vision system, which generates a real-world spatial map of the obstacles and traversable space present in the environment in near real-time. This is done by integrating contextual Awareness and visual SLAM into a ground robotics agent. This paper presents the hardware utilization and performance of the aforementioned system for three different outdoor environments, which represent the applicability of this pipeline to diverse outdoor scenes in near real-time. The entire system is also self-contained and does not require user input, which demonstrates the potential of this computer vision system for autonomous navigation.

Connected through hyperedge HE-1 (induced by source paper "real world mapping of gaze fixations using instance segmentation for road construction safety applications"):
  with members:
    - "real time scene segmentation using a light deep neural network architecture for autonomous robot navigation on construction sites"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "real time scene segmentation using a light deep neural network architecture for autonomous robot navigation on construction sites"):
  with members:
    - "vision based obstacle removal system for autonomous ground vehicles using a robotic arm"
    - "virtual manipulation in an immersive virtual environment simulation of virtual assembly"
    - "utilization of virtual reality visualizations on heavy mobile crane planning for modular construction"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "pruning filters for efficient convnets"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "vision based obstacle removal system for autonomous ground vehicles using a robotic arm"):
  with members:
    - "real time scene segmentation using a light deep neural network architecture for autonomous robot navigation on construction sites"
    - "virtual manipulation in an immersive virtual environment simulation of virtual assembly"

Connected through hyperedge HE-4 (induced by source paper "virtual manipulation in an immersive virtual environment simulation of virtual assembly"):
  with members:
    - "vision based obstacle removal system for autonomous ground vehicles using a robotic arm"
    - "utilization of virtual reality visualizations on heavy mobile crane planning for modular construction"

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)