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 "mean local group average precision mlgap a new performance metric for hashing based retrieval"):
  Abstract: The research on hashing techniques for visual data is gaining increased attention in recent years due to the need for compact representations supporting efficient search/retrieval in large-scale databases such as online images. Among many possibilities, Mean Average Precision(mAP) has emerged as the dominant performance metric for hashing-based retrieval. One glaring shortcoming of mAP is its inability in balancing retrieval accuracy and utilization of hash codes: pushing a system to attain higher mAP will inevitably lead to poorer utilization of the hash codes. Poor utilization of the hash codes hinders good retrieval because of increased collision of samples in the hash space. This means that a model giving a higher mAP values does not necessarily do a better job in retrieval. In this paper, we introduce a new metric named Mean Local Group Average Precision (mLGAP) for better evaluation of the performance of hashing-based retrieval. The new metric provides a retrieval performance measure that also reconciles the utilization of hash codes, leading to a more practically meaningful performance metric than conventional ones like mAP. To this end, we start by mathematical analysis of the deficiencies of mAP for hashing-based retrieval. We then propose mLGAP and show why it is more appropriate for hashing-based retrieval. Experiments on image retrieval are used to demonstrate the effectiveness of the proposed metric.

Includes members:
  Member "simultaneous feature learning and hash coding with deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on deep hashing methods")
  Member "feature learning based deep supervised hashing with pairwise labels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on deep hashing methods")

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)