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: "a general characterization of the statistical query complexity"
  Abstract: Statistical query (SQ) algorithms are algorithms that have access to an {\em SQ oracle} for the input distribution $D$ instead of i.i.d.~ samples from $D$. Given a query function $\phi:X \rightarrow [-1,1]$, the oracle returns an estimate of ${\bf E}_{ x\sim D}[\phi(x)]$ within some tolerance $\tau_\phi$ that roughly corresponds to the number of samples. #R##N#In this work we demonstrate that the complexity of solving general problems over distributions using SQ algorithms can be captured by a relatively simple notion of statistical dimension that we introduce. SQ algorithms capture a broad spectrum of algorithmic approaches used in theory and practice, most notably, convex optimization techniques. Hence our statistical dimension allows to investigate the power of a variety of algorithmic approaches by analyzing a single linear-algebraic parameter. Such characterizations were investigated over the past 20 years in learning theory but prior characterizations are restricted to the much simpler setting of classification problems relative to a fixed distribution on the domain (Blum et al., 1994; Bshouty and Feldman, 2002; Yang, 2001; Simon, 2007; Feldman, 2012; Szorenyi, 2009). Our characterization is also the first to precisely characterize the necessary tolerance of queries. We give applications of our techniques to two open problems in learning theory and to algorithms that are subject to memory and communication constraints.

Connected through hyperedge HE-1 (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"
    - "list decodable learning via sum of squares"
    - "quantum entropy scoring for fast robust mean estimation and improved outlier detection"
    - "high dimensional estimation via sum of squares proofs"

Connected through hyperedge HE-2 (induced by source paper "on the learnability of deep random networks"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "convergence results for neural networks via electrodynamics"
    - "learning non overlapping convolutional neural networks with multiple kernels"
    - "learning neural networks with two nonlinear layers in polynomial time"
    - "when is a convolutional filter easy to learn"
    - "learning depth three neural networks in polynomial time"

Connected through hyperedge HE-3 (induced by source paper "on mean estimation for general norms with statistical queries"):
  with members:
    - "efficient algorithms and lower bounds for robust linear regression"
    - "towards a zero one law for entrywise low rank approximation"
    - "subspace embedding and linear regression with orlicz norm"
    - "on the complexity of learning neural networks"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "approximate near neighbors for general symmetric norms"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "streaming symmetric norms via measure concentration"

Connected through hyperedge HE-4 (induced by source paper "inference under information constraints i lower bounds from chi square contraction"):
  with members:
    - "inference under information constraints ii communication constraints and shared randomness"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "two party distribution testing communication and security"
    - "test without trust optimal locally private distribution testing"
    - "locally private hypothesis testing"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "mutual information optimally local private discrete distribution estimation"

Connected through hyperedge HE-5 (induced by source paper "efficient algorithms and lower bounds for robust linear regression"):
  with members:
    - "efficient algorithms for outlier robust regression"
    - "sever a robust meta algorithm for stochastic optimization"
    - "learning geometric concepts with nasty noise"
    - "outlier robust moment estimation via sum of squares"
    - "being robust in high dimensions can be practical"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "learning from untrusted data"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"

Connected through hyperedge HE-6 (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"

Connected through hyperedge HE-7 (induced by source paper "a general memory bounded learning algorithm"):
  with members:
    - "extractor based time space lower bounds for learning"
    - "time space tradeoffs for learning from small test spaces learning low degree polynomial functions"
    - "mixing complexity and its applications to neural networks"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"

Connected through hyperedge HE-8 (induced by source paper "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"):
  with members:
    - "being robust in high dimensions can be practical"
    - "robust sparse estimation tasks in high dimensions"
    - "robustly learning a gaussian getting optimal error efficiently"
    - "a size free clt for poisson multinomials and its applications"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"
    - "beyond spectral tight bounds for planted gaussians"
    - "a nearly optimal and agnostic algorithm for properly learning a mixture of k gaussians for any constant k"

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)