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: "rates of convergence for nearest neighbor classification"
  Abstract: Nearest neighbor methods are a popular class of nonparametric estimators with several desirable properties, such as adaptivity to different distance scales in different regions of space. Prior work on convergence rates for nearest neighbor classification has not fully reflected these subtle properties. We analyze the behavior of these estimators in metric spaces and provide finite-sample, distribution-dependent rates of convergence under minimal assumptions. As a by-product, we are able to establish the universal consistency of nearest neighbor in a broader range of data spaces than was previously known. We illustrate our upper and lower bounds by introducing smoothness classes that are customized for nearest neighbor classification.

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 "does learning require memorization a short tale about a long tail"):
  with members:
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "two models of double descent for weak features"
    - "harmless interpolation of noisy data in regression"
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "towards demystifying membership inference attacks"
    - "dnn or k nn that is the generalize vs memorize question"
    - "privacy preserving prediction"

Connected through hyperedge HE-3 (induced by source paper "universal bayes consistency in metric spaces"):
  with members:
    - "a new lower bound for agnostic learning with sample compression schemes"
    - "nearest neighbor sample compression efficiency consistency infinite dimensions"
    - "maximum margin multiclass nearest neighbors"
    - "a bayes consistent 1 nn classifier"
    - "on the geometry of similarity search dimensionality curse and concentration of measure"

Connected through hyperedge HE-4 (induced by source paper "fast rates for a knn classifier robust to unknown asymmetric label noise"):
  with members:
    - "on the consistency of exact and approximate nearest neighbor with noisy data"

Connected through hyperedge HE-5 (induced by source paper "nearest neighbor sample compression efficiency consistency infinite dimensions"):
  with members:
    - "active nearest neighbor learning in metric spaces"
    - "maximum margin multiclass nearest neighbors"
    - "a bayes consistent 1 nn classifier"
    - "adaptive metric dimensionality reduction"
    - "on the geometry of similarity search dimensionality curse and concentration of measure"

Connected through hyperedge HE-6 (induced by source paper "near optimal sample compression for nearest neighbors"):
  with members:
    - "nearest neighbor sample compression efficiency consistency infinite dimensions"
    - "active nearest neighbor learning in metric spaces"
    - "maximum margin multiclass nearest neighbors"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "a bayes consistent 1 nn classifier"):
  with members:
    - "near optimal sample compression for nearest neighbors"
    - "maximum margin multiclass nearest neighbors"
    - "efficient classification for metric data"

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)