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: "tight bounds for learning a mixture of two gaussians"
  Abstract: We consider the problem of identifying the parameters of an unknown mixture of two arbitrary $d$-dimensional gaussians from a sequence of independent random samples. Our main results are upper and lower bounds giving a computationally efficient moment-based estimator with an optimal convergence rate, thus resolving a problem introduced by Pearson (1894). Denoting by $\sigma^2$ the variance of the unknown mixture, we prove that $\Theta(\sigma^{12})$ samples are necessary and sufficient to estimate each parameter up to constant additive error when $d=1.$ Our upper bound extends to arbitrary dimension $d>1$ up to a (provably necessary) logarithmic loss in $d$ using a novel---yet simple---dimensionality reduction technique. We further identify several interesting special cases where the sample complexity is notably smaller than our optimal worst-case bound. For instance, if the means of the two components are separated by $\Omega(\sigma)$ the sample complexity reduces to $O(\sigma^2)$ and this is again optimal. #R##N#Our results also apply to learning each component of the mixture up to small error in total variation distance, where our algorithm gives strong improvements in sample complexity over previous work. We also extend our lower bound to mixtures of $k$ Gaussians, showing that $\Omega(\sigma^{6k-2})$ samples are necessary to estimate each parameter up to constant additive error.

Connected through hyperedge HE-1 (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"
    - "robust estimators in high dimensions without the computational intractability"
    - "learning mixtures of gaussians in high dimensions"

Connected through hyperedge HE-2 (induced by source paper "private hypothesis selection"):
  with members:
    - "the optimal approximation factor in density estimation"
    - "differentially private algorithms for learning mixtures of separated gaussians"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "privately learning high dimensional distributions"
    - "locally private gaussian estimation"
    - "private pac learning implies finite littlestone dimension"
    - "learning sums of independent random variables with sparse collective support"
    - "inspectre privately estimating the unseen"

Connected through hyperedge HE-3 (induced by source paper "differentially private algorithms for learning mixtures of separated gaussians"):
  with members:
    - "private hypothesis selection"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "privately learning high dimensional distributions"
    - "differentially private k means with constant multiplicative error"
    - "inspectre privately estimating the unseen"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "finite sample differentially private confidence intervals"
    - "clustering algorithms for the centralized and local models"

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"
    - "list decodable learning via sum of squares"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "trace reconstruction generalized and parameterized"):
  with members:
    - "coded trace reconstruction"
    - "reconstructing trees from traces"
    - "beyond trace reconstruction population recovery from the deletion channel"
    - "mixture models robustness and sum of squares proofs"
    - "learning mixtures of structured distributions over discrete domains"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"

Connected through hyperedge HE-6 (induced by source paper "sample complexity of learning mixtures of sparse linear regressions"):
  with members:
    - "trace reconstruction generalized and parameterized"
    - "learning mixtures of sparse linear regressions using sparse graph codes"
    - "solving a mixture of many random linear equations by tensor decomposition and alternating minimization"
    - "spectral experts for estimating mixtures of linear regressions"
    - "settling the polynomial learnability of mixtures of gaussians"

Connected through hyperedge HE-7 (induced by source paper "mixture models robustness and sum of squares proofs"):
  with members:
    - "thresholding based efficient outlier robust pca"
    - "learning geometric concepts with nasty noise"
    - "outlier robust moment estimation via sum of squares"
    - "being robust in high dimensions can be practical"
    - "on the bit complexity of sum of squares proofs"
    - "exact tensor completion with sum of squares"
    - "better agnostic clustering via relaxed tensor norms"
    - "robustly learning a gaussian getting optimal error efficiently"

Connected through hyperedge HE-8 (induced by source paper "list decodable robust mean estimation and learning mixtures of spherical gaussians"):
  with members:
    - "certified defenses for data poisoning attacks"
    - "learning geometric concepts with nasty noise"
    - "being robust in high dimensions can be practical"
    - "robustly learning a gaussian getting optimal error efficiently"
    - "mixture models robustness and sum of squares proofs"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "on learning mixtures of well separated gaussians"
    - "learning from untrusted data"

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)