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: "robustly learning a gaussian getting optimal error efficiently"
  Abstract: We study the fundamental problem of learning the parameters of a high-dimensional Gaussian in the presence of noise -- where an $\varepsilon$-fraction of our samples were chosen by an adversary. We give robust estimators that achieve estimation error $O(\varepsilon)$ in the total variation distance, which is optimal up to a universal constant that is independent of the dimension. #R##N#In the case where just the mean is unknown, our robustness guarantee is optimal up to a factor of $\sqrt{2}$ and the running time is polynomial in $d$ and $1/\epsilon$. When both the mean and covariance are unknown, the running time is polynomial in $d$ and quasipolynomial in $1/\varepsilon$. Moreover all of our algorithms require only a polynomial number of samples. Our work shows that the same sorts of error guarantees that were established over fifty years ago in the one-dimensional setting can also be achieved by efficient algorithms in high-dimensional settings.

Connected through hyperedge HE-1 (induced by source paper "prophet inequalities with linear correlations and augmentations"):
  with members:
    - "optimal single choice prophet inequalities from samples"
    - "robust algorithms for the secretary problem"
    - "prophet secretary through blind strategies"
    - "stochastic bandits robust to adversarial corruptions"
    - "optimal online contention resolution schemes via ex ante prophet inequalities"
    - "prophet secretary surpassing the 1 1 e barrier"
    - "prophet secretary for combinatorial auctions and matroids"
    - "online allocation with traffic spikes mixing adversarial and stochastic models"

Connected through hyperedge HE-2 (induced by source paper "adversarially robust low dimensional representations"):
  with members:
    - "vc classes are adversarially robustly learnable but only improperly"
    - "how hard is robust mean estimation"
    - "optimal average case reductions to sparse pca from weak assumptions to strong hardness"
    - "on robustness to adversarial examples and polynomial optimization"
    - "approximating operator norms via generalized krivine rounding"
    - "a spectral view of adversarially robust features"
    - "high dimensional robust mean estimation in nearly linear time"
    - "high accuracy low precision training"

Connected through hyperedge HE-3 (induced by source paper "faster algorithms for high dimensional robust covariance estimation"):
  with members:
    - "high dimensional robust sparse regression"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "high dimensional robust mean estimation in nearly linear time"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"

Connected through hyperedge HE-4 (induced by source paper "nearly tight bounds for robust proper learning of halfspaces with a margin"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "vc classes are adversarially robustly learnable but only improperly"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "degree d chow parameters robustly determine degree d ptfs and algorithmic applications"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"

Connected through hyperedge HE-5 (induced by source paper "sgd decentralized byzantine resilience"):
  with members:
    - "distributed learning with adversarial agents under relaxed network condition"
    - "byzantine stochastic gradient descent"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "zeno byzantine suspicious stochastic gradient descent"
    - "federated learning strategies for improving communication efficiency"
    - "adversarial spheres"
    - "finite time guarantees for byzantine resilient distributed state estimation with noisy measurements"

Connected through hyperedge HE-6 (induced by source paper "distribution independent pac learning of halfspaces with massart noise"):
  with members:
    - "efficient algorithms and lower bounds for robust linear regression"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "revisiting perceptron efficient and label optimal learning of halfspaces"
    - "agnostic estimation of mean and covariance"

Connected through hyperedge HE-7 (induced by source paper "an investigation of data poisoning defenses for online learning"):
  with members:
    - "online data poisoning attack"
    - "an optimal control approach to sequential machine teaching"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "sever a robust meta algorithm for stochastic optimization"
    - "data poisoning attacks in contextual bandits"
    - "data poisoning attacks against online learning"
    - "certified defenses for data poisoning attacks"
    - "being robust in high dimensions can be practical"

Connected through hyperedge HE-8 (induced by source paper "list decodable linear regression"):
  with members:
    - "list decodable learning via sum of squares"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "high dimensional robust mean estimation in nearly linear time"
    - "polynomial time tensor decompositions with sum of squares"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "bounded independence fools halfspaces"

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.DS(Data Structures and Algorithms)