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: "sparse logistic regression learns all discrete pairwise graphical models"
  Abstract: We characterize the effectiveness of a classical algorithm for recovering the Markov graph of a general discrete pairwise graphical model from i.i.d. samples. The algorithm is (appropriately regularized) maximum conditional log-likelihood, which involves solving a convex program for each node; for Ising models this is $\ell_1$-constrained logistic regression, while for more general alphabets an $\ell_{2,1}$ group-norm constraint needs to be used. We show that this algorithm can recover any arbitrary discrete pairwise graphical model, and also characterize its sample complexity as a function of model width, alphabet size, edge parameter accuracy, and the number of variables. We show that along every one of these axes, it matches or improves on all existing results and algorithms for this problem. Our analysis applies a sharp generalization error bound for logistic regression when the weight vector has an $\ell_1$ constraint (or $\ell_{2,1}$ constraint) and the sample vector has an $\ell_{\infty}$ constraint (or $\ell_{2, \infty}$ constraint). We also show that the proposed convex programs can be efficiently solved in $\tilde{O}(n^2)$ running time (where $n$ is the number of variables) under the same statistical guarantees. We provide experimental results to support our analysis.

Connected through hyperedge HE-1 (induced by source paper "privately learning markov random fields"):
  with members:
    - "lower bounds for testing graphical models colorings and antiferromagnetic ising models"
    - "private hypothesis selection"
    - "data dependent differentially private parameter learning for directed graphical models"
    - "graphical model based estimation and inference for differential privacy"
    - "privately learning high dimensional distributions"
    - "inspectre privately estimating the unseen"
    - "learning graphical models using multiplicative weights"
    - "differentially private learning of undirected graphical models using collective graphical models"

Connected through hyperedge HE-2 (induced by source paper "efficient learning of discrete graphical models"):
  with members:
    - "learning graphical models using multiplicative weights"
    - "information theoretic properties of markov random fields and their algorithmic applications"
    - "information theoretic optimal learning of gaussian graphical models"
    - "interaction screening efficient and sample optimal learning of ising models"
    - "efficiently learning ising models on arbitrary graphs"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-3 (induced by source paper "learning restricted boltzmann machines with arbitrary external fields"):
  with members:
    - "ising models with latent conditional gaussian variables"
    - "learning restricted boltzmann machines via influence maximization"
    - "learning graphical models using multiplicative weights"
    - "information theoretic properties of markov random fields and their algorithmic applications"
    - "interaction screening efficient and sample optimal learning of ising models"
    - "efficiently learning ising models on arbitrary graphs"

Connected through hyperedge HE-4 (induced by source paper "learning ising models with independent failures"):
  with members:
    - "learning graphical models using multiplicative weights"
    - "information theoretic properties of markov random fields and their algorithmic applications"
    - "interaction screening efficient and sample optimal learning of ising models"
    - "hardness of parameter estimation in graphical models"
    - "efficiently learning ising models on arbitrary graphs"

Connected through hyperedge HE-5 (induced by source paper "learning functional dependencies with sparse regression"):
  with members:
    - "a formal framework for probabilistic unclean databases"
    - "discovering reliable approximate functional dependencies"
    - "holoclean holistic data repairs with probabilistic inference"

Connected through hyperedge HE-6 (induced by source paper "testing ising models"):
  with members:
    - "lower bounds for testing graphical models colorings and antiferromagnetic ising models"
    - "regression from dependent observations"
    - "learning from weakly dependent data under dobrushin s condition"
    - "private identity testing for high dimensional distributions"
    - "learning and testing causal models with interventions"
    - "testing identity of multidimensional histograms"
    - "near optimal closeness testing of discrete histogram distributions"
    - "learning graphical models using multiplicative weights"

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)