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: "similarity based learning via data driven embeddings"
  Abstract: We consider the problem of classification using similarity/distance functions over data. Specifically, we propose a framework for defining the goodness of a (dis)similarity function with respect to a given learning task and propose algorithms that have guaranteed generalization properties when working with such good functions. Our framework unifies and generalizes the frameworks proposed by [Balcan-Blum ICML 2006] and [Wang et al ICML 2007]. An attractive feature of our framework is its adaptability to data - we do not promote a fixed notion of goodness but rather let data dictate it. We show, by giving theoretical guarantees that the goodness criterion best suited to a problem can itself be learned which makes our approach applicable to a variety of domains and problems. We propose a landmarking-based approach to obtaining a classifier from such learned goodness criteria. We then provide a novel diversity based heuristic to perform task-driven selection of landmark points instead of random selection. We demonstrate the effectiveness of our goodness criteria learning method as well as the landmark selection heuristic on a variety of similarity-based learning datasets and benchmark UCI datasets on which our method consistently outperforms existing approaches by a significant margin.

Connected through hyperedge HE-1 (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"
    - "positive semidefinite metric learning using boosting like algorithms"
    - "metric and kernel learning using a linear transformation"

Connected through hyperedge HE-2 (induced by source paper "robustness and generalization for metric learning"):
  with members:
    - "sparse compositional metric learning"
    - "a survey on metric learning for feature vectors and structured data"
    - "guaranteed classification via regularized similarity learning"
    - "similarity learning for provably accurate sparse linear classification"
    - "generalization bounds for metric and similarity learning"
    - "robustness and generalization"

Connected through hyperedge HE-3 (induced by source paper "metric and non metric proximity transformations at linear costs"):
  with members:
    - "efficient algorithms and error analysis for the modified nystrom method"
    - "improving cur matrix decomposition and the nystr o m approximation via adaptive sampling"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "supervised learning with similarity functions"

Connected through hyperedge HE-4 (induced by source paper "guaranteed classification via regularized similarity learning"):
  with members:
    - "generalization guarantees for a binary classification framework for two stage multiple kernel learning"
    - "similarity learning for provably accurate sparse linear classification"
    - "supervised learning with similarity functions"
    - "generalization bounds for metric and similarity learning"
    - "a binary classification framework for two stage multiple kernel learning"
    - "regularization techniques for learning with matrices"

Connected through hyperedge HE-5 (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-6 (induced by source paper "generalization bounds for metric and similarity learning"):
  with members:
    - "learning multi modal similarity"
    - "positive semidefinite metric learning with boosting"

Connected through hyperedge HE-7 (induced by source paper "supervised learning with similarity functions"):
  with members:
    - "support vector machine classification with indefinite kernels"

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)