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 "adversarially robust low dimensional representations"):
  Abstract: Adversarial or test time robustness measures the susceptibility of a machine learning system to small perturbations made to the input at test time. This has attracted much interest on the empirical side, since many existing ML systems perform poorly under imperceptible adversarial perturbations to the test inputs. On the other hand, our theoretical understanding of this phenomenon is limited, and has mostly focused on supervised learning tasks. #R##N#In this work we study the problem of computing adversarially robust representations of data. We formulate a natural extension of Principal Component Analysis (PCA) where the goal is to find a low dimensional subspace to represent the given data with minimum projection error, and that is in addition robust to small perturbations measured in $\ell_q$ norm (say $q=\infty$). Unlike PCA which is solvable in polynomial time, our formulation is computationally intractable to optimize as it captures the well-studied sparse PCA objective. #R##N#We show the following algorithmic and statistical results. #R##N#- Polynomial time algorithms in the worst-case that achieve constant factor approximations to the objective while only violating the robustness constraint by a constant factor. #R##N#- We prove that our formulation (and algorithms) also enjoy significant statistical benefits in terms of sample complexity over standard PCA on account of a "regularization effect", that is formalized using the well-studied spiked covariance model. #R##N#- Surprisingly, we show that our algorithmic techniques can also be made robust to corruptions in the training data, in addition to yielding representations that are robust at test time! Here an adversary is allowed to corrupt potentially every data point up to a specified amount in the $\ell_q$ norm. We further apply these techniques for mean estimation and clustering under adversarial corruptions to the training data.

Includes members:
  Member "vc classes are adversarially robustly learnable but only improperly":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "more data can expand the generalization gap between adversarially robust and standard models")
  Member "how hard is robust mean estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust mean estimation under coordinate level corruption")
  Member "optimal average case reductions to sparse pca from weak assumptions to strong hardness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "average case lower bounds for learning sparse mixtures robust estimation and semirandom adversaries")
  Member "on robustness to adversarial examples and polynomial optimization":
    also connected through other hyperedges:
      (none)
  Member "approximating operator norms via generalized krivine rounding":
    also connected through other hyperedges:
      (none)
  Member "a spectral view of adversarially robust features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "high dimensional robust mean estimation in nearly linear time":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "positive semidefinite programming mixed parallel and width independent")
  Member "high accuracy low precision training":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse weight activation training")

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.DS(Data Structures and Algorithms)