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: "relief r cnn utilizing convolutional features for fast object detection"
  Abstract: R-CNN style methods are sorts of the state-of-the-art object detection methods, which consist of region proposal generation and deep CNN classification. However, the proposal generation phase in this paradigm is usually time consuming, which would slow down the whole detection time in testing. This paper suggests that the value discrepancies among features in deep convolutional feature maps contain plenty of useful spatial information, and proposes a simple approach to extract the information for fast region proposal generation in testing. The proposed method, namely Relief R-CNN (R2-CNN), adopts a novel region proposal generator in a trained R-CNN style model. The new generator directly generates proposals from convolutional features by some simple rules, thus resulting in a much faster proposal generation speed and a lower demand of computation resources. Empirical studies show that R2-CNN could achieve the fastest detection speed with comparable accuracy among all the compared algorithms in testing.

Connected through hyperedge HE-1 (induced by source paper "towards symbolic reinforcement learning with common sense"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "deep reinforcement learning an overview"
    - "logic tensor networks for semantic image interpretation"
    - "towards deep symbolic reinforcement learning"
    - "building machines that learn and think like people"
    - "spatial transformer networks"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "gaussian processes with context supported priors for active object localization"):
  with members:
    - "active object localization in visual situations"
    - "how far are we from solving pedestrian detection"
    - "deep feature based contextual model for object detection"
    - "recovering hard to find object instances by sampling context based object proposals"
    - "fast on line kernel density estimation for active object localization"
    - "searching for objects using structure in indoor scenes"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "uavs using bayesian optimization to locate wifi devices"

Connected through hyperedge HE-3 (induced by source paper "towards deep symbolic reinforcement learning"):
  with members:
    - "progressive neural networks"
    - "successor features for transfer in reinforcement learning"
    - "classifying options for deep reinforcement learning"
    - "continuous deep q learning with model based acceleration"
    - "strategic attentive writer for learning macro actions"
    - "building machines that learn and think like people"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "tagger deep unsupervised perceptual grouping"

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)