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: "bing a fast high quality object proposal generator at 100fps"
  Abstract: We are motivated by the need for an object proposal generation algorithm that achieves a good balance between proposal localization quality, object recall and computational efficiency. We propose a novel object proposal algorithm {\em BING++} which inherits the good computational efficiency of BING \cite{BingObj2014} but significantly improves its proposal localization quality. Central to our success is based on the observation that good bounding boxes are those that tightly cover objects. Edge features, which can be computed efficiently, play a critical role in this context. We propose a new algorithm that recursively improves BING's proposals by exploiting the fact that edges in images are typically associated with object boundaries. BING++ improves proposals recursively by incorporating nearest edge points (to proposal boundary pixels) to obtain a tighter bounding box. This operation has linear computational complexity in number of pixels and can be done efficiently using distance transform. Superpixel merging techniques are then employed as post-processing to further improve the proposal quality. Empirically on the VOC2007 dataset, using $10^3$ proposals and IoU threshold 0.5, our method achieves 95.3\% object detection recall (DR), 79.2\% mean average best overlap (MABO), and 68.7\% mean average precision (mAP) on object detection over 20 object classes within an average time of {\bf 0.009} seconds per image.

Connected through hyperedge HE-1 (induced by source paper "zero shot detection"):
  with members:
    - "probabilistic semantic retrieval for surveillance videos with activity graphs"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "generalized zero shot recognition based on visually semantic embedding"
    - "recent advances in zero shot recognition"
    - "focal loss for dense object detection"
    - "zero shot learning the good the bad and the ugly"
    - "mask r cnn"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-2 (induced by source paper "richer convolutional features for edge detection"):
  with members:
    - "deep crisp boundaries from boundaries to higher level tasks"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "hi fi hierarchical feature integration for skeleton detection"
    - "webseg learning semantic segmentation from web searches"
    - "learning deep structured multi scale features using attention gated crfs for contour prediction"
    - "casenet deep category aware semantic edge detection"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "convolutional oriented boundaries"

Connected through hyperedge HE-3 (induced by source paper "dynamic zoom in network for fast object detection in large images"):
  with members:
    - "remotenet efficient relevant motion event detection for large scale home surveillance videos"
    - "nisp pruning networks using neuron importance score propagation"
    - "c wsl count guided weakly supervised localization"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "mask r cnn"
    - "generating holistic 3d scene abstractions for text based image retrieval"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-4 (induced by source paper "saliency detection for improving object proposals"):
  with members:
    - "salient object detection a survey"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "what makes for effective detection proposals"
    - "inner and inter label propagation salient object detection in the wild"
    - "salient object detection a benchmark"
    - "fast r cnn"
    - "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)