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: "list decodable learning via sum of squares"
  Abstract: In the list-decodable learning setup, an overwhelming majority (say a $1-\beta$-fraction) of the input data consists of outliers and the goal of an algorithm is to output a small list $\mathcal{L}$ of hypotheses such that one of them agrees with inliers. We develop a framework for list-decodable learning via the Sum-of-Squares SDP hierarchy and demonstrate it on two basic statistical estimation problems #R##N#{\it Linear regression:} Suppose we are given labelled examples $\{(X_i,y_i)\}_{i \in [N]}$ containing a subset $S$ of $\beta N$ {\it inliers} $\{X_i \}_{i \in S}$ that are drawn i.i.d. from standard Gaussian distribution $N(0,I)$ in $\mathbb{R}^d$, where the corresponding labels $y_i$ are well-approximated by a linear function $\ell$. We devise an algorithm that outputs a list $\mathcal{L}$ of linear functions such that there exists some $\hat{\ell} \in \mathcal{L}$ that is close to $\ell$. #R##N#This yields the first algorithm for linear regression in a list-decodable setting. Our results hold for any distribution of examples whose concentration and anticoncentration can be certified by Sum-of-Squares proofs. #R##N#{\it Mean Estimation:} #R##N#Given data points $\{X_i\}_{i \in [N]}$ containing a subset $S$ of $\beta N$ {\it inliers} $\{X_i \}_{i \in S}$ that are drawn i.i.d. from a Gaussian distribution $N(\mu,I)$ in $\mathbb{R}^d$, we devise an algorithm that generates a list $\mathcal{L}$ of means such that there exists $\hat{\mu} \in \mathcal{L}$ close to $\mu$. #R##N#The recovery guarantees of the algorithm are analogous to the existing algorithms for the problem by Diakonikolas \etal and Kothari \etal. #R##N#In an independent and concurrent work, Karmalkar \etal \cite{KlivansKS19} also obtain an algorithm for list-decodable linear regression using the Sum-of-Squares SDP hierarchy.

Connected through hyperedge HE-1 (induced by source paper "list decodable subspace recovery"):
  with members:
    - "list decodable linear regression"
    - "recent advances in algorithmic high dimensional robust statistics"
    - "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"
    - "resilience a criterion for learning in the presence of arbitrary outliers"

Connected through hyperedge HE-2 (induced by source paper "list decodable subspace recovery via sum of squares"):
  with members:
    - "an overview of robust subspace recovery"
    - "high dimensional robust mean estimation in nearly linear time"
    - "static and dynamic robust pca and matrix completion a review"
    - "better agnostic clustering via relaxed tensor norms"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"
    - "robust subspace clustering"

Connected through hyperedge HE-3 (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"
    - "quantum entropy scoring for fast robust mean estimation and improved outlier detection"
    - "high dimensional estimation via sum of squares proofs"
    - "high dimensional robust sparse regression"

Connected through hyperedge HE-4 (induced by source paper "learning mixtures of linear regressions in subexponential time via fourier moments"):
  with members:
    - "list decodable linear regression"
    - "em converges for a mixture of many linear regressions"
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "learning mixtures of linear regressions with nearly optimal complexity"
    - "hyperplane clustering via dual principal component pursuit"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"
    - "learning from untrusted data"
    - "fourier sparse interpolation without a frequency gap"

Connected through hyperedge HE-5 (induced by source paper "efficiently learning structured distributions from untrusted batches"):
  with members:
    - "list decodable linear regression"
    - "high dimensional estimation via sum of squares proofs"
    - "federated learning strategies for improving communication efficiency"
    - "being robust in high dimensions can be practical"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "learning discrete distributions from untrusted batches"
    - "learning populations of parameters"
    - "mixture models robustness and sum of squares proofs"

Connected through hyperedge HE-6 (induced by source paper "generalized list decoding"):
  with members:
    - "list decodable linear regression"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "list decodable zero rate codes"
    - "an improved bound on the fraction of correctable deletions"
    - "upper bound on list decoding radius of binary codes"
    - "a characterization of the capacity of online causal binary channels"
    - "it ll probably work out improved list decoding through random operations"
    - "list decoding reed muller codes over small fields"

Connected through hyperedge HE-7 (induced by source paper "list decodable linear regression"):
  with members:
    - "efficient algorithms and lower bounds for robust linear regression"
    - "high dimensional robust mean estimation in nearly linear time"
    - "robustly learning a gaussian getting optimal error efficiently"
    - "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)