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: "parametric local metric learning for nearest neighbor classification"
  Abstract: We study the problem of learning local metrics for nearest neighbor classification. Most previous works on local metric learning learn a number of local unrelated metrics. While this "independence" approach delivers an increased flexibility its downside is the considerable risk of overfitting. We present a new parametric local metric learning method in which we learn a smooth metric matrix function over the data manifold. Using an approximation error bound of the metric matrix function we learn local metrics as linear combinations of basis metrics defined on anchor points over different regions of the instance space. We constrain the metric matrix function by imposing on the linear combinations manifold regularization which makes the learned metric matrix function vary smoothly along the geodesics of the data manifold. Our metric learning method has excellent performance both in terms of predictive power and scalability. We experimented with several large-scale classification problems, tens of thousands of instances, and compared it with several state of the art metric learning methods, both global and local, as well as to SVM with automatic kernel selection, all of which it outperforms in a significant manner.

Connected through hyperedge HE-1 (induced by source paper "local distance metric learning for nearest neighbor algorithm"):
  with members:
    - "learning local invariant mahalanobis distances"
    - "a survey on metric learning for feature vectors and structured data"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-2 (induced by source paper "learning to rank using localized geometric mean metrics"):
  with members:
    - "similarity learning for high dimensional sparse data"
    - "the greedy miser learning under test time budgets"
    - "learning content similarity for music recommendation"
    - "positive semidefinite metric learning using boosting like algorithms"

Connected through hyperedge HE-3 (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"
    - "positive semidefinite metric learning using boosting like algorithms"

Connected through hyperedge HE-4 (induced by source paper "constraint selection in metric learning"):
  with members:
    - "deep metric learning via lifted structured feature embedding"
    - "sparse compositional metric learning"
    - "similarity based learning via data driven embeddings"
    - "positive semidefinite metric learning using boosting like algorithms"
    - "metric and kernel learning using a linear transformation"

Connected through hyperedge HE-5 (induced by source paper "nonlinear metric learning for knn and svms through geometric transformations"):
  with members:
    - "f svm combination of feature transformation and svm learning via convex relaxation"
    - "deep learning face representation by joint identification verification"
    - "deep metric learning using triplet network"
    - "a survey on metric learning for feature vectors and structured data"
    - "information theoretic semi supervised metric learning via entropy regularization"

Connected through hyperedge HE-6 (induced by source paper "learning local invariant mahalanobis distances"):
  with members:
    - "a survey on metric learning for feature vectors and structured data"

Connected through hyperedge HE-7 (induced by source paper "sparse compositional metric learning"):
  with members:
    - "robustness and generalization for metric learning"
    - "a survey on metric learning for feature vectors and structured data"
    - "positive semidefinite metric learning using boosting like algorithms"
    - "robustness and generalization"

Connected through hyperedge HE-8 (induced by source paper "scalable similarity learning using large margin neighborhood embedding"):
  with members:
    - "learning fine grained image similarity with deep ranking"
    - "recovering the optimal solution by dual random projection"
    - "supervised metric learning with generalization guarantees"

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.LG(Machine Learning)