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 "high dimensional robust m estimation arbitrary corruption and heavy tails"):
  Abstract: We consider the problem of sparsity-constrained $M$-estimation when both explanatory and response variables have heavy tails (bounded 4-th moments), or a fraction of arbitrary corruptions. We focus on the $k$-sparse, high-dimensional regime where the number of variables $d$ and the sample size $n$ are related through $n \sim k \log d$. We define a natural condition we call the Robust Descent Condition (RDC), and show that if a gradient estimator satisfies the RDC, then Robust Hard Thresholding (IHT using this gradient estimator), is guaranteed to obtain good statistical rates. The contribution of this paper is in showing that this RDC is a flexible enough concept to recover known results, and obtain new robustness results. Specifically, new results include: (a) For $k$-sparse high-dimensional linear- and logistic-regression with heavy tail (bounded 4-th moment) explanatory and response variables, a linear-time-computable median-of-means gradient estimator satisfies the RDC, and hence Robust Hard Thresholding is minimax optimal; (b) When instead of heavy tails we have $O(1/\sqrt{k}\log(nd))$-fraction of arbitrary corruptions in explanatory and response variables, a near linear-time computable trimmed gradient estimator satisfies the RDC, and hence Robust Hard Thresholding is minimax optimal. We demonstrate the effectiveness of our approach in sparse linear, logistic regression, and sparse precision matrix estimation on synthetic and real-world US equities data.

Includes members:
  Member "high dimensional robust sparse regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks")
  Member "compressed sensing with adversarial sparse noise via l1 regression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing")
  Member "learning with bad training data via iterative trimmed loss minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dividemix learning with noisy labels as semi supervised learning")
  Member "securing distributed machine learning in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "threats to federated learning a survey")
  Member "iteratively learning from the best":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks")
  Member "efficient algorithms for outlier robust regression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing")
  Member "byzantine robust distributed learning towards optimal statistical rates":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "turbo aggregate breaking the quadratic aggregation barrier in secure federated learning")
  Member "sever a robust meta algorithm for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the effectiveness of mitigating data poisoning attacks with gradient shaping")

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)