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: "on the consistency of exact and approximate nearest neighbor with noisy data"
  Abstract: Nearest neighbor has been one of the simplest and most appealing non-parametric approaches in machine learning, pattern recognition, computer vision, etc. Empirical studies show the resistance of k-nearest neighbor to noise, yet the theoretical understanding is not clear. This work presents the consistency analysis on exact and approximate nearest neighbor in the random noise setting. Our theoretical studies show that k-nearest neighbor, in the noise setting, gets the same consistent rate as that in the noise-free setting, which verifies the robustness of k-nearest neighbor to random noise. The nearest neighbor (1-NN), however, is proven to be biased by random noise. For approximate $k$-nearest neighbor, we first generalize the Johnson-Lindenstrauss lemma to infinite set, and based on this result, we show that the approximate $k$-nearest neighbor is also robust to random noise as that of the exact k-nearest neighbor, and achieves faster convergence rate yet with a tradeoff between consistency and reduced dimension. Specifically, approximate $k$-nearest neighbor with sharp dimensional reduction tends to cause large deviation from the Bayes risk.

Connected through hyperedge HE-1 (induced by source paper "minimum norm adversarial examples on knn and knn based models"):
  with members:
    - "on the robustness of deep k nearest neighbors"
    - "defense against adversarial images using web scale nearest neighbor search"
    - "adversarial examples for non parametric methods attacks defenses and large sample limits"
    - "defending against adversarial examples with k nearest neighbor"
    - "fast rates for a knn classifier robust to unknown asymmetric label noise"
    - "adversarial training with voronoi constraints"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "towards the first adversarially robust neural network model on mnist"

Connected through hyperedge HE-2 (induced by source paper "fast rates for a knn classifier robust to unknown asymmetric label noise"):
  with members:
    - "rates of convergence for nearest neighbor classification"

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)