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: "the optimal approximation factor in density estimation"
  Abstract: Consider the following problem: given two arbitrary densities $q_1,q_2$ and a sample-access to an unknown target density $p$, find which of the $q_i$'s is closer to $p$ in total variation. #R##N#A remarkable result due to Yatracos shows that this problem is tractable in the following sense: there exists an algorithm that uses $O(\epsilon^{-2})$ samples from $p$ and outputs~$q_i$ such that with high probability, $TV(q_i,p) \leq 3\cdot\mathsf{opt} + \epsilon$, where $\mathsf{opt}= \min\{TV(q_1,p),TV(q_2,p)\}$. Moreover, this result extends to any finite class of densities $\mathcal{Q}$: there exists an algorithm that outputs the best density in $\mathcal{Q}$ up to a multiplicative approximation factor of 3. #R##N#We complement and extend this result by showing that: (i) the factor 3 can not be improved if one restricts the algorithm to output a density from $\mathcal{Q}$, and (ii) if one allows the algorithm to output arbitrary densities (e.g.\ a mixture of densities from $\mathcal{Q}$), then the approximation factor can be reduced to 2, which is optimal. In particular this demonstrates an advantage of improper learning over proper in this setup. #R##N#We develop two approaches to achieve the optimal approximation factor of 2: an adaptive one and a static one. Both approaches are based on a geometric point of view of the problem and rely on estimating surrogate metrics to the total variation. Our sample complexity bounds exploit techniques from {\it Adaptive Data Analysis}.

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 "private mean estimation of heavy tailed distributions"):
  with members:
    - "private hypothesis selection"
    - "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"
    - "finite sample differentially private confidence intervals"

Connected through hyperedge HE-3 (induced by source paper "supervised learning no loss no cry"):
  with members:
    - "learning effective loss functions efficiently"
    - "a tunable loss function for classification"
    - "learning surrogate losses"
    - "a scaled bregman theorem with applications"
    - "relative loss bounds for on line density estimation with the exponential family of distributions"

Connected through hyperedge HE-4 (induced by source paper "private hypothesis selection"):
  with members:
    - "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"
    - "the structure of optimal private tests for simple hypotheses"

Connected through hyperedge HE-5 (induced by source paper "deconstructing generative adversarial networks"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "approximability of discriminators implies diversity in gans"
    - "a convex duality framework for gans"
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on the implicit assumptions of gans"
    - "solving approximate wasserstein gans to stationarity"
    - "multi agent diverse generative adversarial networks"

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)