human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "positive semidefinite metric learning with boosting"
  Abstract: The learning of appropriate distance metrics is a critical problem in image classification and retrieval. In this work, we propose a boosting-based technique, termed \BoostMetric, for learning a Mahalanobis distance metric. One of the primary difficulties in learning such a metric is to ensure that the Mahalanobis matrix remains positive semidefinite. Semidefinite programming is sometimes used to enforce this constraint, but does not scale well. \BoostMetric is instead based on a key observation that any positive semidefinite matrix can be decomposed into a linear positive combination of trace-one rank-one matrices. \BoostMetric thus uses rank-one positive semidefinite matrices as weak learners within an efficient and scalable boosting-based learning process. The resulting method is easy to implement, does not require tuning, and can accommodate various types of constraints. Experiments on various datasets show that the proposed algorithm compares favorably to those state-of-the-art methods in terms of classification accuracy and running time.

Connected through hyperedge HE-1 (induced by source paper "a survey on metric learning for feature vectors and structured data"):
  with members:
    - "robustness and generalization for metric learning"
    - "efficient distance metric learning by adaptive sampling and mini batch stochastic gradient descent sgd"
    - "large margin metric learning for partitioning problems"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"
    - "guaranteed classification via regularized similarity learning"
    - "similarity learning for provably accurate sparse linear classification"
    - "parametric local metric learning for nearest neighbor classification"
    - "a conditional random field for discriminatively trained finite state string edit distance"

Connected through hyperedge HE-2 (induced by source paper "a kernel classification framework for metric learning"):
  with members:
    - "efficient distance metric learning by adaptive sampling and mini batch stochastic gradient descent sgd"
    - "information theoretic semi supervised metric learning via entropy regularization"
    - "learning neighborhoods for metric learning"
    - "positive semidefinite metric learning using boosting like algorithms"
    - "scalable large margin mahalanobis distance metric learning"
    - "metric and kernel learning using a linear transformation"

Connected through hyperedge HE-3 (induced by source paper "supervised metric learning with generalization guarantees"):
  with members:
    - "robustness and generalization for metric learning"
    - "similarity learning for provably accurate sparse linear classification"
    - "information theoretic semi supervised metric learning via entropy regularization"
    - "a conditional random field for discriminatively trained finite state string edit distance"
    - "supervised learning with similarity functions"
    - "non sparse linear representations for visual tracking with online reservoir metric learning"
    - "minimizing the misclassification error rate using a surrogate convex loss"
    - "adaptive regularization for weight matrices"

Connected through hyperedge HE-4 (induced by source paper "multimodal similarity preserving hashing"):
  with members:
    - "descriptor learning for omnidirectional image matching"
    - "learning multi modal similarity"

Connected through hyperedge HE-5 (induced by source paper "generalization bounds for metric and similarity learning"):
  with members:
    - "similarity based learning via data driven embeddings"
    - "learning multi modal similarity"

Connected through hyperedge HE-6 (induced by source paper "non sparse linear representations for visual tracking with online reservoir metric learning"):
  with members:
    - "generalized kernel based visual tracking"

Connected through hyperedge HE-7 (induced by source paper "learning discriminative metrics via generative models and kernel learning"):
  with members:
    - "l2 regularization for learning kernels"

Connected through hyperedge HE-8 (induced by source paper "positive semidefinite metric learning using boosting like algorithms"):
  with members:
    - "on the dual formulation of boosting algorithms"

Question: Please tell me which class the center node 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)