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 "robust estimators in high dimensions without the computational intractability"):
  Abstract: We study high-dimensional distribution learning in an agnostic setting where an adversary is allowed to arbitrarily corrupt an e-fraction of the samples. Such questions have a rich history spanning statistics, machine learning and theoretical computer science. Even in the most basic settings, the only known approaches are either computationally inefficient or lose dimension-dependent factors in their error guarantees. This raises the following question: Is high-dimensional agnostic distribution learning even possible, algorithmically? In this work, we obtain the first computationally efficient algorithms with dimension-independent error guarantees for agnostically learning several fundamental classes of high-dimensional distributions: (1) a single Gaussian, (2) a product distribution on the hypercube, (3) mixtures of two product distributions (under a natural balancedness condition), and (4) mixtures of spherical Gaussians. Our algorithms achieve error that is independent of the dimension, and in many cases scales nearly-linearly with the fraction of adversarially corrupted samples. Moreover, we develop a general recipe for detecting and correcting corruptions in high-dimensions, that may be applicable to many other problems. Part of this work was performed while the author was at the University of Edinburgh. Research supported in part by EPSRC grant EP/L021749/1, a Marie Curie Career Integration Grant, and a SICSA grant. Supported by NSF Award CCF-0953960 (CAREER) and ONR grant N00014-12-1-0999. This work was done in part while the author was an intern at Microsoft Research Cambridge and visiting the Simons Institute for the Theory of Computing. Part of this work was performed while visiting the University of Edinburgh. Supposed by NSF CAREER Award CCF-1453261, a Google Faculty Research Award, and an NSF Fellowship. This work was done in part while the author was an intern at Microsoft Research Cambridge. Supported by NSF CAREER Award CCF-1453261, a grant from the MIT NEC Corporation, and a Google Faculty Research Award. Part of this work was performed while the author was at the University of Edinburgh. Research supported in part by EPSRC grant EP/L021749/1.

Includes members:
  Member "being robust in high dimensions can be practical":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "private mean estimation of heavy tailed distributions")
  Member "robust sparse estimation tasks in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "average case lower bounds for learning sparse mixtures robust estimation and semirandom adversaries")
  Member "list decodable robust mean estimation and learning mixtures of spherical gaussians":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "meta learning for mixed linear regression")
  Member "resilience a criterion for learning in the presence of arbitrary outliers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning structured distributions from untrusted batches faster and simpler")
  Member "learning from untrusted data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning structured distributions from untrusted batches faster and simpler")
  Member "a size free clt for poisson multinomials and its applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "private hypothesis selection")

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)