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: "nearest neighbor sample compression efficiency consistency infinite dimensions"
  Abstract: We examine the Bayes-consistency of a recently proposed 1-nearest-neighbor-based multiclass learning algorithm. This algorithm is derived from sample compression bounds and enjoys the statistical advantages of tight, fully empirical generalization bounds, as well as the algorithmic advantages of a faster runtime and memory savings. We prove that this algorithm is strongly Bayes-consistent in metric spaces with finite doubling dimension --- the first consistency result for an efficient nearest-neighbor sample compression scheme. Rather surprisingly, we discover that this algorithm continues to be Bayes-consistent even in a certain infinite-dimensional setting, in which the basic measure-theoretic conditions on which classic consistency proofs hinge are violated. This is all the more surprising, since it is known that $k$-NN is not Bayes-consistent in this setting. We pose several challenging open problems for future research.

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 "when are non parametric methods robust"):
  with members:
    - "universal bayes consistency in metric spaces"
    - "adversarially robust submodular maximization under knapsack constraints"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "robust decision trees against adversarial examples"
    - "on the robustness of deep k nearest neighbors"
    - "adversarial examples for non parametric methods attacks defenses and large sample limits"
    - "provably robust boosted decision stumps and trees against adversarial attacks"
    - "adversarially robust generalization requires more data"

Connected through hyperedge HE-3 (induced by source paper "natively interpretable machine learning and artificial intelligence preliminary results and future directions"):
  with members:
    - "effects of distance measure choice on k nearest neighbor classifier performance a review"
    - "evaluating neural network explanation methods using hybrid documents and morphological agreement"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"
    - "why should i trust you explaining the predictions of any classifier"
    - "maximizing diversity in biology and beyond"
    - "deep learning and the information bottleneck principle"
    - "falling rule lists"

Connected through hyperedge HE-4 (induced by source paper "universal bayes consistency in metric spaces"):
  with members:
    - "a new lower bound for agnostic learning with sample compression schemes"
    - "maximum margin multiclass nearest neighbors"
    - "rates of convergence for nearest neighbor classification"
    - "a bayes consistent 1 nn classifier"
    - "on the geometry of similarity search dimensionality curse and concentration of measure"

Connected through hyperedge HE-5 (induced by source paper "near optimal sample compression for nearest neighbors"):
  with members:
    - "active nearest neighbor learning in metric spaces"
    - "maximum margin multiclass nearest neighbors"
    - "rates of convergence for nearest neighbor classification"
    - "a bayes consistent 1 nn classifier"
    - "efficient classification for metric data"
    - "adaptive metric dimensionality reduction"
    - "efficient regression in metric spaces via approximate lipschitz extension"
    - "fast construction of nets in low dimensional metrics and their applications"

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)