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 "optimizing top precision performance measure of content based image retrieval by learning similarity function"):
  Abstract: In this paper we study the problem of content-based image retrieval. In this problem, the most popular performance measure is the top precision measure, and the most important component of a retrieval system is the similarity function used to compare a query image against a database image. However, up to now, there is no existing similarity learning method proposed to optimize the top precision measure. To fill this gap, in this paper, we propose a novel similarity learning method to maximize the top precision measure. We model this problem as a minimization problem with an objective function as the combination of the losses of the relevant images ranked behind the top-ranked irrelevant image, and the squared Frobenius norm of the similarity function parameter. This minimization problem is solved as a quadratic programming problem. The experiments over two benchmark data sets show the advantages of the proposed method over other similarity learning methods when the top precision is used as the performance measure.

Includes members:
  Member "supervised learning of sparse context reconstruction coefficients for data representation and classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stochastic learning of multi instance dictionary for earth mover s distance based histogram comparison")
  Member "push and pull based epidemic spreading in networks thresholds and deeper insights":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "metrics towards measuring cyber agility")
  Member "representing data by sparse combination of contextual data points for classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse coding with earth mover s distance for multi instance histogram representation")
  Member "remote health coaching system and human motion data analysis for physical therapy with microsoft kinect":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey of visual analysis of human motion and its applications")
  Member "evaluation of pose tracking accuracy in the first and second generations of microsoft kinect":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cardiopulmonary resuscitation quality parameters from motion capture data using differential evolution fitting of sinusoids")
  Member "image tag completion by local learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semi supervised structured output prediction by local linear regression and sub gradient descent")
  Member "supervised cross modal factor analysis for multiple modal data classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cross model convolutional neural network for multiple modality data representation")
  Member "unsupervised temporal segmentation of repetitive human actions based on kinematic modeling and frequency analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "energy based periodicity mining with deep features for action repetition counting in unconstrained videos")

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)