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: "efficient classification for metric data"
  Abstract: Recent advances in large-margin classification of data residing in general metric spaces (rather than Hilbert spaces) enable classification under various natural metrics, such as string edit and earthmover distance. A general framework developed for this purpose by von Luxburg and Bousquet [JMLR, 2004] left open the questions of computational efficiency and of providing direct bounds on generalization error. #R##N#We design a new algorithm for classification in general metric spaces, whose runtime and accuracy depend on the doubling dimension of the data points, and can thus achieve superior classification performance in many common scenarios. The algorithmic core of our approach is an approximate (rather than exact) solution to the classical problems of Lipschitz extension and of Nearest Neighbor Search. The algorithm's generalization performance is guaranteed via the fat-shattering dimension of Lipschitz classifiers, and we present experimental evidence of its superiority to some common kernel methods. As a by-product, we offer a new perspective on the nearest neighbor classifier, which yields significantly sharper risk asymptotics than the classic analysis of Cover and Hart [IEEE Trans. Info. Theory, 1967].

Connected through hyperedge HE-1 (induced by source paper "robustness for non parametric classification a generic attack and defense"):
  with members:
    - "provable certificates for adversarial examples fitting a ball in the union of polytopes"
    - "robust decision trees against adversarial examples"
    - "on the robustness of deep k nearest neighbors"
    - "theoretically principled trade off between robustness and accuracy"
    - "provably robust boosted decision stumps and trees against adversarial attacks"
    - "on evaluating adversarial robustness"
    - "adversarial training with voronoi constraints"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"

Connected through hyperedge HE-2 (induced by source paper "a general coreset based approach to diversity maximization under matroid constraints"):
  with members:
    - "diversity maximization in doubling metrics"
    - "local search for max sum diversification"
    - "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"

Connected through hyperedge HE-3 (induced by source paper "diversity in combinatorial optimization"):
  with members:
    - "what is known about vertex cover kernelization"
    - "propagation via kernelization the vertex cover constraint"
    - "guarantees and limits of preprocessing in constraint satisfaction and reasoning"
    - "parameterized complexity and kernel bounds for hard planning problems"
    - "limits of preprocessing"
    - "kernels for global constraints"

Connected through hyperedge HE-4 (induced by source paper "wegotyoucovered the winning solver from the pace 2019 implementation challenge vertex cover track"):
  with members:
    - "scalable kernelization for maximum independent sets"
    - "exact algorithms for maximum independent set"
    - "accelerating local search for the maximum independent set problem"
    - "on the power of simple reductions for the maximum independent set problem"
    - "trajectory based dynamic map labeling"
    - "linear time fpt algorithms via network flow"

Connected through hyperedge HE-5 (induced by source paper "classification in asymmetric spaces via sample compression"):
  with members:
    - "a constant factor approximation algorithm for the asymmetric traveling salesman problem"
    - "effective resistance reducing flows spectrally thin trees and asymmetric tsp"
    - "maximum margin multiclass nearest neighbors"
    - "quasi metrics similarities and searches aspects of geometry of protein datasets"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-6 (induced by source paper "improved generalization bounds for robust learning"):
  with members:
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "optimal bounds on the vc dimension"
    - "adversarial risk and robustness general definitions and implications for the uniform distribution"
    - "adversarially robust generalization requires more data"
    - "rademacher complexity for adversarially robust generalization"
    - "adversarial machine learning at scale"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "evasion attacks against machine learning at test time"

Connected through hyperedge HE-7 (induced by source paper "what is known about vertex cover kernelization"):
  with members:
    - "smaller parameters for vertex cover kernelization"
    - "vertex cover structural parameterization revisited"
    - "deleting edges to restrict the size of an epidemic a new application for treewidth"
    - "matchings with lower quotas algorithms and complexity"
    - "kernelization lower bounds by cross composition"
    - "representative sets and irrelevant vertices new tools for kernelization"
    - "vertex cover kernelization revisited upper and lower bounds for a refined parameter"

Connected through hyperedge HE-8 (induced by source paper "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"):
  with members:
    - "the traveling salesman problem low dimensionality implies a polynomial time approximation scheme"
    - "a practical parallel algorithm for diameter approximation of massive weighted graphs"
    - "max sum diversity via convex programming"
    - "space and time efficient parallel graph decomposition clustering and diameter approximation"
    - "space round tradeoffs for mapreduce computations"
    - "maximum dispersion and geometric maximum weight cliques"

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)