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 "a revisit of hashing algorithms for approximate nearest neighbor search"):
  Abstract: Approximate Nearest Neighbor Search (ANNS) is a fundamental problem in many areas of machine learning and data mining. During the past decade, numerous hashing algorithms are proposed to solve this problem. Every proposed algorithm claims outperform other state-of-the-art hashing methods. However, the evaluation of these hashing papers was not thorough enough, and those claims should be re-examined. The ultimate goal of an ANNS method is returning the most accurate answers (nearest neighbors) in the shortest time. If implemented correctly, almost all the hashing methods will have their performance improved as the code length increases. However, many existing hashing papers only report the performance with the code length shorter than 128. In this paper, we carefully revisit the problem of search with a hash index, and analyze the pros and cons of two popular hash index search procedures. Then we proposed a very simple but effective two level index structures and make a thorough comparison of eleven popular hashing algorithms. Surprisingly, the random-projection-based Locality Sensitive Hashing (LSH) is the best performed algorithm, which is in contradiction to the claims in all the other ten hashing papers. Despite the extreme simplicity of random-projection-based LSH, our results show that the capability of this algorithm has been far underestimated. For the sake of reproducibility, all the codes used in the paper are released on GitHub, which can be used as a testing platform for a fair comparison between various hashing algorithms.

Includes members:
  Member "fast approximate nearest neighbor search with navigating spreading out graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "efanna an extremely fast approximate nearest neighbor search algorithm based on knn graph":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to route in similarity graphs")
  Member "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "approximate nearest neighbor search on high dimensional data experiments analyses and improvement v1 0":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beyond precision a study on recall of initial retrieval with neural representations")
  Member "hashing for similarity search a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on deep hashing methods")
  Member "self taught hashing for fast similarity search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning compressed sentence representations for on device text processing")

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)