human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "bihl a fast and high performance object proposals based on binarized hl frequency"):
  Abstract: In recent years, the use of object proposal as a preprocessing step for target detection to improve computational efficiency has become an effective method. Good object proposal methods should have high object detection recall rate and low computational cost, as well as good location accuracy and repeatability. However, it is difficult for current advanced algorithms to achieve a good balance in the above performance. Therefore, it is especially important to ensure that the recall rate and location quality are not degraded while accelerating object generation.For this problem, we propose a class-independent object proposal algorithm BIHL. It combines the advantages of window scoring and superpixel merging. First, a binarized horizontal high frequency component feature and a linear classifier are used to learn and generate a set of candidate boxs with a objective score. Then, the candidate boxs are merged based on the principle of location and score proximity. Different from superpixel merging algorithm, our method does not use pixel level operation to avoid a lot of computation without losing performance. Experimental results on the VOC2007 dataset and the VOC2007 synthetic interference dataset containing 297,120 test images show that when including difficult-to-identify objects with an IOU threshold of 0.5 and 10000 budget proposals, our method achieves a 99.3% detection recall and an mean average best overlap of 81.1% . The average processing time of our method for all test set images is 0.0015 seconds, which is nearly 3 times faster than the current fastest method. In repeatability testing, our method is the method with the highest average repeatability among the methods that achieve good repeatability to various disturbances, and the average repeatability is 10% higher than RPN. The code will be published in {this https URL}

Includes members:
  Member "cascade r cnn delving into high quality object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "proposal learning for semi supervised object detection")
  Member "multiscale combinatorial grouping for image segmentation and object proposal generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "what makes for effective detection proposals":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deeply activated salient region for instance search")
  Member "learning to segment object candidates":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

Question: Please tell me which class the source paper 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)