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 "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.

Includes members:
  Member "list decodable linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "list decodable subspace recovery")
  Member "efficient algorithms and lower bounds for robust linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing")
  Member "efficient algorithms for outlier robust regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing")
  Member "outlier robust moment estimation via sum of squares":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "list decodable subspace recovery")
  Member "better agnostic clustering via relaxed tensor norms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "list decodable subspace recovery")
  Member "list decodable robust mean estimation and learning mixtures of spherical gaussians":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "meta learning for mixed linear regression")
  Member "mixture models robustness and sum of squares proofs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "meta learning for mixed linear regression")
  Member "learning from untrusted data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "prophet inequalities with linear correlations and augmentations")

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)