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 using boosting like algorithms"
  Abstract: The success of many machine learning and pattern recognition methods relies heavily upon the identification of an appropriate distance metric on the input data. It is often beneficial to learn such a metric from the input training data, instead of using a default one such as the Euclidean distance. In this work, we propose a boosting-based technique, termed BoostMetric, for learning a quadratic Mahalanobis distance metric. Learning a valid Mahalanobis distance metric requires enforcing the constraint that the matrix parameter to the metric remains positive definite. Semidefinite programming is often used to enforce this constraint, but does not scale well and easy to implement. BoostMetric is instead based on the observation that any positive semidefinite matrix can be decomposed into a linear 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 methods are easy to implement, efficient, and can accommodate various types of constraints. We extend traditional boosting algorithms in that its weak learner is a positive semidefinite matrix with trace and rank being one rather than a classifier or regressor. Experiments on various datasets demonstrate that the proposed algorithms compare 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 "deep metric learning with bier boosting independent embeddings robustly"):
  with members:
    - "incremental boosting convolutional neural network for facial action unit recognition"
    - "adversarial discriminative domain adaptation"
    - "deep metric learning with angular loss"
    - "no fuss distance metric learning using proxies"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "smart mining for deep metric learning"
    - "siamese instance search for tracking"
    - "deep metric learning via facility location"

Connected through hyperedge HE-2 (induced by source paper "metric learning with dynamically generated pairwise constraints for ear recognition"):
  with members:
    - "a survey on metric learning for feature vectors and structured data"

Connected through hyperedge HE-3 (induced by source paper "person re identification via recurrent feature aggregation"):
  with members:
    - "recurrent attention models for depth based person identification"
    - "person re identification by discriminative selection in video ranking"
    - "person re identification meets image search"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "caffe convolutional architecture for fast feature embedding"
    - "human re identification by matching compositional template with cluster sampling"

Connected through hyperedge HE-4 (induced by source paper "learning to rank using localized geometric mean metrics"):
  with members:
    - "similarity learning for high dimensional sparse data"
    - "parametric local metric learning for nearest neighbor classification"
    - "the greedy miser learning under test time budgets"
    - "learning content similarity for music recommendation"

Connected through hyperedge HE-5 (induced by source paper "dari distance metric and representation integration for person verification"):
  with members:
    - "deep feature learning with relative distance comparison for person re identification"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "person re identification by local maximal occurrence representation and metric learning"
    - "learning fine grained image similarity with deep ranking"
    - "recurrent convolutional neural networks for scene parsing"
    - "human re identification by matching compositional template with cluster sampling"
    - "parametric local metric learning for nearest neighbor classification"

Connected through hyperedge HE-6 (induced by source paper "structured learning of binary codes with column generation"):
  with members:
    - "fast supervised hashing with decision trees for high dimensional data"
    - "optimizing ranking measures for compact binary code learning"
    - "learning hash functions using column generation"
    - "inductive hashing on manifolds"
    - "a general two step approach to learning based hashing"
    - "structboost boosting methods for predicting structured output variables"
    - "on the dual formulation of boosting algorithms"

Connected through hyperedge HE-7 (induced by source paper "constraint selection in metric learning"):
  with members:
    - "deep metric learning via lifted structured feature embedding"
    - "sparse compositional metric learning"
    - "parametric local metric learning for nearest neighbor classification"
    - "similarity based learning via data driven embeddings"
    - "metric and kernel learning using a linear transformation"

Connected through hyperedge HE-8 (induced by source paper "learning to rank in person re identification with metric ensembles"):
  with members:
    - "mid level deep pattern mining"
    - "pedestrian detection with spatially pooled features and structured ensemble learning"
    - "deep convolutional neural fields for depth estimation from a single image"

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)