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: "rotation ensemble module for detecting rotation invariant features"
  Abstract: Deep learning has improved many computer vision tasks by utilizing data-driven features instead of using hand-crafted features. However, geometric transformations of input images often degrade the performance of deep learning based methods. In particular, rotation-invariant features are important in computer vision tasks such as face detection, biological feature detection of microscopy images, or robot grasp detection since the input image can be fed into the network with any rotation angle. In this paper, we propose rotation ensemble module (REM) to efficiently train and utilize rotation-invariant features in a deep neural network for computer vision tasks. We evaluated our proposed REM with face detection tasks on FDDB dataset, robotic grasp detection tasks on Cornell dataset, and real robotic grasp tasks with several novel objects. REM based face detection deep neural networks yielded up to 50.8% accuracy in face detection task on FDDB dataset at false rate 20 with IOU 75%, which is about 10.7% higher than the baseline. Robotic grasp detection deep neural networks with our REM also yielded up to 97.6% accuracy in robotic grasp detection on Cornell dataset that is higher than current state-of-the-art performance. In robotic grasp task using a real 4-axis robotic arm with several novel objects, our REM based robotic grasp achieved up to 93.8%, which is significantly higher than the baseline robotic grasps (11.0-56.3%).

Connected through hyperedge HE-1 (induced by source paper "gq stn optimizing one shot grasp detection based on robustness classifier"):
  with members:
    - "dealing with ambiguity in robotic grasping via multiple predictions"
    - "fully convolutional grasp detection network with oriented anchor box"
    - "roi based robotic grasp detection in object overlapping scenes using convolutional neural network"
    - "classification based grasp detection using spatial transformer network"
    - "learning object localization and 6d pose estimation from simulation and weakly labeled real images"
    - "jacquard a large scale dataset for robotic grasp detection"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "robotic grasp detection using deep convolutional neural 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.CV(Computer Vision and Pattern Recognition)