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: "object proposal generation using two stage cascade svms"
  Abstract: Object proposal algorithms have shown great promise as a first step for object recognition and detection. Good object proposal generation algorithms require high object recall rate as well as low computational cost, because generating object proposals is usually utilized as a preprocessing step. The problem of how to accelerate the object proposal generation and evaluation process without decreasing recall is thus of great interest. In this paper, we propose a new object proposal generation method using two-stage cascade SVMs, where in the first stage linear filters are learned for predefined quantized scales/aspect-ratios independently, and in the second stage a global linear classifier is learned across all the quantized scales/aspect-ratios for calibration, so that all the proposals can be compared properly. The proposals with highest scores are our final output. Specifically, we explain our scale/aspect-ratio quantization scheme, and investigate the effects of combinations of $\ell_1$ and $\ell_2$ regularizers in cascade SVMs with/without ranking constraints in learning. Comprehensive experiments on VOC2007 dataset are conducted, and our results achieve the state-of-the-art performance with high object recall rate and high computational efficiency. Besides, our method has been demonstrated to be suitable for not only class-specific but also generic object proposal generation.

Connected through hyperedge HE-1 (induced by source paper "a new target specific object proposal generation method for visual tracking"):
  with members:
    - "object discovery via cohesion measurement"
    - "sanet structure aware network for visual tracking"
    - "hypernet towards accurate region proposal generation and joint object detection"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "return of the devil in the details delving deep into convolutional nets"
    - "fast edge detection using structured forests"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "object proposal with kernelized partial ranking"):
  with members:
    - "generalized min max kernel and generalized consistent weighted sampling"
    - "what makes for effective detection proposals"
    - "linearized gmm kernels and normalized random fourier features"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "scalable object detection using deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "sequential optimization for efficient high quality object proposal generation"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "scale aware pixelwise object proposal networks"
    - "what makes for effective detection proposals"
    - "group membership prediction"
    - "fast r cnn"
    - "deepproposal hunting objects by cascading deep convolutional layers"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "an exploration of parameter redundancy in deep networks with circulant projections"

Connected through hyperedge HE-4 (induced by source paper "bing a fast high quality object proposal generator at 100fps"):
  with members:
    - "how good are detection proposals really"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)