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 "semi supervised structured output prediction by local linear regression and sub gradient descent"):
  Abstract: We propose a novel semi-supervised structured output prediction method based on local linear regression in this paper. The existing semi-supervise structured output prediction methods learn a global predictor for all the data points in a data set, which ignores the differences of local distributions of the data set, and the effects to the structured output prediction. To solve this problem, we propose to learn the missing structured outputs and local predictors for neighborhoods of different data points jointly. Using the local linear regression strategy, in the neighborhood of each data point, we propose to learn a local linear predictor by minimizing both the complexity of the predictor and the upper bound of the structured prediction loss. The minimization problem is solved by sub-gradient descent algorithms. We conduct experiments over two benchmark data sets, and the results show the advantages of the proposed method.

Includes members:
  Member "optimizing top precision performance measure of content based image retrieval by learning similarity function":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a novel image tag completion method based on convolutional neural network")
  Member "manifold regularization in structured output space for semi supervised structured output prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semi supervised learning of local structured output predictors")
  Member "image tag completion by local learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimizing top precision performance measure of content based image retrieval by learning similarity function")
  Member "supervised cross modal factor analysis for multiple modal data classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimizing top precision performance measure of content based image retrieval by learning similarity function")
  Member "multiple kernel multivariate performance learning using cutting plane algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimizing top precision performance measure of content based image retrieval by learning similarity function")

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.LG(Machine Learning)