human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "certified robustness to label flipping attacks via randomized smoothing"):
  Abstract: Machine learning algorithms are known to be susceptible to data poisoning attacks, where an adversary manipulates the training data to degrade performance of the resulting classifier. While many heuristic defenses have been proposed, few defenses exist which are certified against worst-case corruption of the training data. In this work, we propose a strategy to build linear classifiers that are certifiably robust against a strong variant of label-flipping, where each test example is targeted independently. In other words, for each test point, our classifier makes a prediction and includes a certification that its prediction would be the same had some number of training labels been changed adversarially. Our approach leverages randomized smoothing, a technique that has previously been used to guarantee---with high probability---test-time robustness to adversarial manipulation of the input to a classifier. We derive a variant which provides a deterministic, analytical bound, sidestepping the probabilistic certificates that traditionally result from the sampling subprocedure. Further, we obtain these certified bounds with no additional runtime cost over standard classification. We generalize our results to the multi-class case, providing what we believe to be the first multi-class classification algorithm that is certifiably robust to label-flipping attacks.

Includes members:
  Member "tight certificates of adversarial robustness for randomly smoothed classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "black box smoothing a provable defense for pretrained classifiers")
  Member "on defending against label flipping attacks on malware detection systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "certified adversarial robustness via randomized smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "provably robust deep learning via adversarially trained smoothed classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "compressed sensing with adversarial sparse noise via l1 regression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "high dimensional robust m estimation arbitrary corruption and heavy tails")
  Member "learning with bad training data via iterative trimmed loss minimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "efficient algorithms and lower bounds for robust linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust mean estimation under coordinate level corruption")
  Member "representer point selection for explaining deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on completeness aware concept based explanations in deep neural networks")

Question: Please tell me which class the source paper 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)