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: "sequential optimization for efficient high quality object proposal generation"
  Abstract: We are motivated by the need for a generic object proposal generation algorithm which achieves good balance between object detection recall, proposal localization quality and computational efficiency. We propose a novel object proposal algorithm, BING++, which inherits the virtue of good computational efficiency of BING but significantly improves its proposal localization quality. At high level we formulate the problem of object proposal generation from a novel probabilistic perspective, based on which our BING++ manages to improve the localization quality by employing edges and segments to estimate object boundaries and update the proposals sequentially. We propose learning the parameters efficiently by searching for approximate solutions in a quantized parameter space for complexity reduction. We demonstrate the generalization of BING++ with the same fixed parameters across different object classes and datasets. Empirically our BING++ can run at half speed of BING on CPU, but significantly improve the localization quality by 18.5% and 16.7% on both VOC2007 and Microhsoft COCO datasets, respectively. Compared with other state-of-the-art approaches, BING++ can achieve comparable performance, but run significantly faster.

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 "a scalable pipelined dataflow accelerator for object region proposals on fpga platform"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "ssd single shot multibox detector"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "group membership prediction"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "3d object proposals using stereo imagery for accurate object class detection"):
  with members:
    - "vehicle detection from 3d lidar using fully convolutional network"
    - "fast detection of multiple objects in traffic scenes with a common detection framework"
    - "what makes for effective detection proposals"
    - "learning to detect vehicles by clustering appearance patterns"
    - "filtered channel features for pedestrian detection"
    - "towards scene understanding with detailed 3d object representations"
    - "segdeepm exploiting segmentation and context in deep neural networks for object detection"
    - "fast r cnn"

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)