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: "convolutional neural network based image representation for visual loop closure detection"
  Abstract: Deep convolutional neural networks (CNN) have recently been shown in many computer vision and pattern recog- nition applications to outperform by a significant margin state- of-the-art solutions that use traditional hand-crafted features. However, this impressive performance is yet to be fully exploited in robotics. In this paper, we focus one specific problem that can benefit from the recent development of the CNN technology, i.e., we focus on using a pre-trained CNN model as a method of generating an image representation appropriate for visual loop closure detection in SLAM (simultaneous localization and mapping). We perform a comprehensive evaluation of the outputs at the intermediate layers of a CNN as image descriptors, in comparison with state-of-the-art image descriptors, in terms of their ability to match images for detecting loop closures. The main conclusions of our study include: (a) CNN-based image representations perform comparably to state-of-the-art hand- crafted competitors in environments without significant lighting change, (b) they outperform state-of-the-art competitors when lighting changes significantly, and (c) they are also significantly faster to extract than the state-of-the-art hand-crafted features even on a conventional CPU and are two orders of magnitude faster on an entry-level GPU.

Connected through hyperedge HE-1 (induced by source paper "fast loop closure detection via binary content"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "a photometrically calibrated benchmark for monocular visual odometry"
    - "pcanet a simple deep learning baseline for image classification"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-2 (induced by source paper "spatio semantic convnet based visual place recognition"):
  with members:
    - "a holistic visual place recognition approach using lightweight cnns for severe viewpoint and appearance changes"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "convolutional neural network architecture for geometric matching"
    - "deep learning features at scale for visual place recognition"
    - "bags of local convolutional features for scalable instance search"
    - "a taxonomy of deep convolutional neural nets for computer vision"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-3 (induced by source paper "fast and incremental loop closure detection using proximity graphs"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "visual place recognition with probabilistic voting"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "optimal data dependent hashing for approximate near neighbors"
    - "on the performance of convnet features for place recognition"
    - "an efficient index for visual search in appearance based slam"

Connected through hyperedge HE-4 (induced by source paper "visual global localization with a hybrid wnn cnn approach"):
  with members:
    - "geometric loss functions for camera pose regression with deep learning"
    - "towards cnn map compression for camera relocalisation"
    - "topometric localization with deep learning"
    - "relative camera pose estimation using convolutional neural networks"
    - "planet photo geolocation with convolutional neural networks"
    - "dsac differentiable ransac for camera localization"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "learning to see by moving"

Connected through hyperedge HE-5 (induced by source paper "cnn feature boosted seqslam for real time loop closure detection"):
  with members:
    - "on the performance of convnet features for place recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "beyond sift using binary features for loop closure detection"):
  with members:
    - "mild multi index hashing based loop closure detection"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-7 (induced by source paper "towards cognitive exploration through deep reinforcement learning for mobile robots"):
  with members:
    - "continuous deep q learning with model based acceleration"
    - "benchmarking deep reinforcement learning for continuous control"
    - "continuous control with deep reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"
    - "deep recurrent q learning for partially observable mdps"
    - "towards vision based deep reinforcement learning for robotic motion control"
    - "model based reinforcement learning with parametrized physical models and optimism driven exploration"
    - "caffe convolutional architecture for fast feature embedding"

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)