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: "person following by autonomous robots a categorical overview"
  Abstract: A wide range of human-robot collaborative applications in industry, search and rescue operations, healthcare, and social interactions require an autonomous robot to follow its human companion. Different operating mediums and applications pose diverse challenges by adding constraints on the choice of sensors, the degree of autonomy, and dynamics of the person following robot. Researchers have addressed these challenges in many ways and contributed to the development of a large body of literature. This paper provides a comprehensive overview of the literature by categorizing different aspects of person-following by autonomous robots. Also, the corresponding operational challenges are identified based on various design choices for ground, underwater, and aerial scenarios. In addition, state-of-the-art methods for perception, planning, control, and interaction are elaborately discussed, and their feasibilities are evaluated in terms of standard operational and performance metrics. Furthermore, several prospective application areas are identified, and open problems are highlighted for future research.

Connected through hyperedge HE-1 (induced by source paper "machine vision for improved human robot cooperation in adverse underwater conditions"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "real world underwater enhancement challenges benchmarks and solutions"
    - "fast underwater image enhancement for improved visual perception"
    - "robot to robot relative pose estimation using humans as markers"
    - "deep visual attention prediction"
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "underwater surveying via bearing only cooperative localization"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "real time multi diver tracking and re identification for underwater human robot collaboration"):
  with members:
    - "cvpr19 tracking and detection challenge how crowded can it get"
    - "deep cosine metric learning for person re identification"
    - "visual diver recognition for underwater human robot collaboration"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "enhancing underwater imagery using generative adversarial networks"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"
    - "simple online and realtime tracking with a deep association metric"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

Connected through hyperedge HE-3 (induced by source paper "robot to robot relative pose estimation using humans as markers"):
  with members:
    - "underwater surveying via bearing only cooperative localization"
    - "densepose dense human pose estimation in the wild"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "deepcut joint subset partition and labeling for multi person pose estimation"
    - "deeppose human pose estimation via deep neural networks"
    - "exact global histogram specification optimized for structural similarity"

Connected through hyperedge HE-4 (induced by source paper "vision based control of a quadrotor in user proximity mediated vs end to end learning approaches"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "learning to navigate autonomously in outdoor environments mavnet"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "limits of end to end learning"
    - "learning to fly by crashing"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "deep steering learning end to end driving model from spatial and temporal visual cues"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"

Connected through hyperedge HE-5 (induced by source paper "towards a generic diver following algorithm balancing robustness and efficiency in deep visual detection"):
  with members:
    - "enhancing underwater imagery using generative adversarial networks"
    - "dynamic reconfiguration of mission parameters in underwater human robot collaboration"
    - "underwater multi robot convoying using visual tracking by detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "yolo9000 better faster stronger"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-6 (induced by source paper "visual diver recognition for underwater human robot collaboration"):
  with members:
    - "enhancing underwater imagery using generative adversarial networks"
    - "dynamic reconfiguration of mission parameters in underwater human robot collaboration"
    - "underwater multi robot convoying using visual tracking by detection"
    - "an implementation of faster rcnn with study for region sampling"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-7 (induced by source paper "understanding human motion and gestures for underwater human robot collaboration"):
  with members:
    - "enhancing underwater imagery using generative adversarial networks"
    - "dynamic reconfiguration of mission parameters in underwater human robot collaboration"
    - "underwater multi robot convoying using visual tracking by detection"
    - "yolo9000 better faster stronger"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "robot communication via motion closing the underwater human robot interaction loop"):
  with members:
    - "robust gesture based communication for underwater human robot interaction in the context of search and rescue diver missions"
    - "enhancing underwater imagery using generative adversarial networks"
    - "dynamic reconfiguration of mission parameters in underwater human robot collaboration"

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)