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: "dynamic reconfiguration of mission parameters in underwater human robot collaboration"
  Abstract: This paper presents a real-time programming and parameter reconfiguration method for autonomous underwater robots in human-robot collaborative tasks. Using a set of intuitive and meaningful hand gestures, we develop a syntactically simple framework that is computationally more efficient than a complex, grammar-based approach. In the proposed framework, a convolutional neural network is trained to provide accurate hand gesture recognition; subsequently, a finite-state machine-based deterministic model performs efficient gesture-to-instruction mapping, and further improves robustness of the interaction scheme. The key aspect of this framework is that it can be easily adopted by divers for communicating simple instructions to underwater robots without using artificial tags such as fiducial markers, or requiring them to memorize a potentially complex set of language rules. Extensive experiments are performed both on field-trial data and through simulation, which demonstrate the robustness, efficiency, and portability of this framework in a number of different scenarios. Finally, a user interaction study is presented that illustrates the gain in usability of our proposed interaction framework compared to the existing methods for underwater domains.

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 "by land air or sea multi domain robot communication via motion"):
  with members:
    - "robust gesture based communication for underwater human robot interaction in the context of search and rescue diver missions"
    - "robot communication via motion closing the underwater human robot interaction loop"
    - "a review of verbal and non verbal human robot interactive communication"

Connected through hyperedge HE-3 (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"
    - "underwater multi robot convoying using visual tracking by detection"
    - "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-4 (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"
    - "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-5 (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"
    - "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-6 (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"
    - "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-7 (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"
    - "person following by autonomous robots a categorical overview"
    - "enhancing underwater imagery using generative adversarial networks"

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)