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: "objectness scoring and detection proposals in forward looking sonar images with convolutional neural networks"
  Abstract: Forward-looking sonar can capture high resolution images of underwater scenes, but their interpretation is complex. Generic object detection in such images has not been solved, specially in cases of small and unknown objects. In comparison, detection proposal algorithms have produced top performing object detectors in real-world color images. In this work we develop a Convolutional Neural Network that can reliably score objectness of image windows in forward-looking sonar images and by thresholding objectness, we generate detection proposals. In our dataset of marine garbage objects, we obtain 94 % recall, generating around 60 proposals per image. The biggest strength of our method is that it can generalize to previously unseen objects. We show this by detecting chain links, walls and a wrench without previous training in such objects. We strongly believe our method can be used for class-independent object detection, with many real-world applications such as chain following and mine detection.

Connected through hyperedge HE-1 (induced by source paper "results from the robocademy itn autonomy disturbance rejection and perception for advanced marine robotics"):
  with members:
    - "robust trajectory tracking control for underactuated autonomous underwater vehicles"
    - "decentralized impedance control for cooperative manipulation of multiple underwater vehicle manipulator systems under lean communication"
    - "a distributed predictive control approach for cooperative manipulation of multiple underwater vehicle manipulator systems"
    - "improving sonar image patch matching via deep learning"
    - "decentralized abstractions and timed constrained planning of a general class of coupled multi agent systems"
    - "a nonlinear model predictive control scheme for cooperative manipulation with singularity and collision avoidance"
    - "a robust model predictive control approach for autonomous underwater vehicles operating in a constrained workspace"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-2 (induced by source paper "learning objectness from sonar images for class independent object detection"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "real time convolutional networks for sonar image classification in low power embedded systems"
    - "what makes for effective detection proposals"
    - "object proposal evaluation protocol is gameable"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"
    - "how good are detection proposals really"

Connected through hyperedge HE-3 (induced by source paper "real time convolutional networks for sonar image classification in low power embedded systems"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "learning both weights and connections for efficient neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "exploiting linear structure within convolutional networks for efficient evaluation"
    - "convolutional neural networks at constrained time cost"

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.CV(Computer Vision and Pattern Recognition)