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: "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.

Connected through hyperedge HE-1 (induced by source paper "locally private hypothesis selection"):
  with members:
    - "private hypothesis selection"
    - "pan private uniformity testing"
    - "sorted top k in rounds"
    - "improved differentially private analysis of variance"
    - "locally private learning without interaction requires separation"
    - "exponential separations in local differential privacy through communication complexity"
    - "the role of interactivity in local differential privacy"
    - "private identity testing for high dimensional distributions"

Connected through hyperedge HE-2 (induced by source paper "teaser fast and certifiable point cloud registration"):
  with members:
    - "did you miss the sign a false negative alarm system for traffic sign detectors"
    - "consensus maximization tree search revisited"
    - "a polynomial time solution for robust registration with extreme outlier rates"
    - "a practical maximum clique algorithm for matching with pairwise constraints"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "deep closest point learning representations for point cloud registration"
    - "prnet self supervised learning for partial to partial registration"
    - "fast and globally optimal rigid registration of 3d point sets by transformation decomposition"

Connected through hyperedge HE-3 (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-4 (induced by source paper "private mean estimation of heavy tailed distributions"):
  with members:
    - "private hypothesis selection"
    - "the optimal approximation factor in density estimation"
    - "differentially private algorithms for learning mixtures of separated gaussians"
    - "privately learning high dimensional distributions"
    - "locally private gaussian estimation"
    - "inspectre privately estimating the unseen"
    - "being robust in high dimensions can be practical"
    - "differentially private testing of identity and closeness of discrete distributions"

Connected through hyperedge HE-5 (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-6 (induced by source paper "em algorithm is sample optimal for learning mixtures of well separated gaussians"):
  with members:
    - "em converges for a mixture of many linear regressions"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "sample efficient learning of mixtures"
    - "mixture models robustness and sum of squares proofs"
    - "on learning mixtures of well separated gaussians"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "learning mixtures of gaussians in high dimensions"
    - "tight bounds for learning a mixture of two gaussians"

Connected through hyperedge HE-7 (induced by source paper "on the sample complexity of adversarial multi source pac learning"):
  with members:
    - "optimal robust learning of discrete distributions from batches"
    - "byzantine stochastic gradient descent"
    - "do outliers ruin collaboration"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "mitigating sybils in federated learning poisoning"
    - "defending against saddle point attack in byzantine robust distributed learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"

Connected through hyperedge HE-8 (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"

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)