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: "underwater multi robot convoying using visual tracking by detection"
  Abstract: We present a robust multi-robot convoying approach that relies on visual detection of the leading agent, thus enabling target following in unstructured 3-D environments. Our method is based on the idea of tracking-by-detection, which interleaves efficient model-based object detection with temporal filtering of image-based bounding box estimation. This approach has the important advantage of mitigating tracking drift (i.e. drifting away from the target object), which is a common symptom of model-free trackers and is detrimental to sustained convoying in practice. To illustrate our solution, we collected extensive footage of an underwater robot in ocean settings, and hand-annotated its location in each frame. Based on this dataset, we present an empirical comparison of multiple tracker variants, including the use of several convolutional neural networks, both with and without recurrent connections, as well as frequency-based model-free trackers. We also demonstrate the practicality of this tracking-by-detection strategy in real-world scenarios by successfully controlling a legged underwater robot in five degrees of freedom to follow another robot's independent motion.

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 "physical adversarial textures that fool visual object tracking"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "a general framework for adversarial examples with objectives"
    - "deepbillboard systematic physical world testing of autonomous driving systems"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "end to end representation learning for correlation filter based tracking"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "detect to track and track to detect"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "morphology agnostic visual robotic control"):
  with members:
    - "replab a reproducible low cost arm benchmark platform for robotic learning"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "contingency aware exploration in reinforcement learning"
    - "towards a generic diver following algorithm balancing robustness and efficiency in deep visual detection"
    - "learning visual servoing with deep features and fitted q iteration"
    - "self supervised visual planning with temporal skip connections"
    - "learning a visuomotor controller for real world robotic grasping using easily simulated depth images"
    - "sim2real view invariant visual servoing by recurrent control"

Connected through hyperedge HE-4 (induced by source paper "person following by autonomous robots a categorical overview"):
  with members:
    - "visual diver recognition for underwater human robot collaboration"
    - "robot communication via motion closing the underwater human robot interaction loop"
    - "caddy underwater stereo vision dataset for human robot interaction hri in the context of diver activities"
    - "enhancing underwater imagery using generative adversarial networks"
    - "dynamic reconfiguration of mission parameters in underwater human robot collaboration"
    - "real time 3d human tracking for mobile robots with multisensors"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "socially aware motion planning with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "enhancing underwater imagery using generative adversarial networks"):
  with members:
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "instance normalization the missing ingredient for fast stylization"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "energy based generative adversarial network"
    - "least squares generative adversarial networks"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "towards a generic diver following algorithm balancing robustness and efficiency in deep visual detection"):
  with members:
    - "person following by autonomous robots a categorical overview"
    - "enhancing underwater imagery using generative adversarial networks"
    - "dynamic reconfiguration of mission parameters in underwater human robot collaboration"
    - "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-7 (induced by source paper "visual diver recognition for underwater human robot collaboration"):
  with members:
    - "person following by autonomous robots a categorical overview"
    - "enhancing underwater imagery using generative adversarial networks"
    - "dynamic reconfiguration of mission parameters in underwater human robot collaboration"
    - "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-8 (induced by source paper "understanding human motion and gestures for underwater human robot collaboration"):
  with members:
    - "person following by autonomous robots a categorical overview"
    - "enhancing underwater imagery using generative adversarial networks"
    - "dynamic reconfiguration of mission parameters in underwater human robot collaboration"
    - "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"

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)