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: "learning from untrusted data"
  Abstract: The vast majority of theoretical results in machine learning and statistics assume that the available training data is a reasonably reliable reflection of the phenomena to be learned or estimated. Similarly, the majority of machine learning and statistical techniques used in practice are brittle to the presence of large amounts of biased or malicious data. In this work we consider two frameworks in which to study estimation, learning, and optimization in the presence of significant fractions of arbitrary data. #R##N#The first framework, list-decodable learning, asks whether it is possible to return a list of answers, with the guarantee that at least one of them is accurate. For example, given a dataset of $n$ points for which an unknown subset of $\alpha n$ points are drawn from a distribution of interest, and no assumptions are made about the remaining $(1-\alpha)n$ points, is it possible to return a list of $\operatorname{poly}(1/\alpha)$ answers, one of which is correct? The second framework, which we term the semi-verified learning model, considers the extent to which a small dataset of trusted data (drawn from the distribution in question) can be leveraged to enable the accurate extraction of information from a much larger but untrusted dataset (of which only an $\alpha$-fraction is drawn from the distribution). #R##N#We show strong positive results in both settings, and provide an algorithm for robust learning in a very general stochastic optimization setting. This general result has immediate implications for robust estimation in a number of settings, including for robustly estimating the mean of distributions with bounded second moments, robustly learning mixtures of such distributions, and robustly finding planted partitions in random graphs in which significant portions of the graph have been perturbed by an adversary.

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"
    - "robustly learning a gaussian getting optimal error efficiently"
    - "prophet secretary for combinatorial auctions and matroids"

Connected through hyperedge HE-2 (induced by source paper "learning structured distributions from untrusted batches faster and simpler"):
  with members:
    - "optimal robust learning of discrete distributions from batches"
    - "recent advances in algorithmic high dimensional robust statistics"
    - "efficiently learning structured distributions from untrusted batches"
    - "quantum entropy scoring for fast robust mean estimation and improved outlier detection"
    - "federated learning strategies for improving communication efficiency"
    - "sever a robust meta algorithm for stochastic optimization"
    - "being robust in high dimensions can be practical"
    - "learning discrete distributions from untrusted batches"

Connected through hyperedge HE-3 (induced by source paper "list decodable subspace recovery"):
  with members:
    - "list decodable linear regression"
    - "recent advances in algorithmic high dimensional robust statistics"
    - "list decodable learning via sum of squares"
    - "outlier robust moment estimation via sum of squares"
    - "better agnostic clustering via relaxed tensor norms"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "bayesian estimation from few samples community detection and related problems"
    - "mixture models robustness and sum of squares proofs"

Connected through hyperedge HE-4 (induced by source paper "robust mean estimation under coordinate level corruption"):
  with members:
    - "faster algorithms for high dimensional robust covariance estimation"
    - "holodetect few shot learning for error detection"
    - "how hard is robust mean estimation"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "being robust in high dimensions can be practical"
    - "holoclean holistic data repairs with probabilistic inference"

Connected through hyperedge HE-5 (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-6 (induced by source paper "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"):
  with members:
    - "understanding impacts of high order loss approximations and features in deep learning interpretation"
    - "strip a defence against trojan attacks on deep neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "explaining deep learning models a bayesian non parametric approach"
    - "sentinet detecting physical attacks against deep learning systems"
    - "textbugger generating adversarial text against real world applications"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"

Connected through hyperedge HE-7 (induced by source paper "using pre training can improve model robustness and uncertainty"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "logit pairing methods can fool gradient based attacks"
    - "feature denoising for improving adversarial robustness"
    - "dimensionality driven learning with noisy labels"
    - "deep anomaly detection with outlier exposure"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "imbalanced deep learning by minority class incremental rectification"
    - "generalisation in humans and deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "recent advances in algorithmic high dimensional robust statistics"):
  with members:
    - "faster algorithms for high dimensional robust covariance estimation"
    - "list decodable linear regression"
    - "high dimensional robust estimation of sparse models via trimmed hard thresholding"
    - "average case lower bounds for learning sparse mixtures robust estimation and semirandom adversaries"
    - "how hard is robust mean estimation"
    - "list decodable learning via sum of squares"
    - "quantum entropy scoring for fast robust mean estimation and improved outlier detection"
    - "high dimensional estimation via sum of squares proofs"

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)