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: "on learning discrete graphical models using greedy methods"
  Abstract: In this paper, we address the problem of learning the structure of a pairwise graphical model from samples in a high-dimensional setting. Our first main result studies the sparsistency, or consistency in sparsity pattern recovery, properties of a forward-backward greedy algorithm as applied to general statistical models. As a special case, we then apply this algorithm to learn the structure of a discrete graphical model via neighborhood estimation. As a corollary of our general result, we derive sufficient conditions on the number of samples n, the maximum node-degree d and the problem size p, as well as other conditions on the model parameters, so that the algorithm recovers all the edges with high probability. Our result guarantees graph selection for samples scaling as n = Omega(d^2 log(p)), in contrast to existing convex-optimization based algorithms that require a sample complexity of \Omega(d^3 log(p)). Further, the greedy algorithm only requires a restricted strong convexity condition which is typically milder than irrepresentability assumptions. We corroborate these results using numerical simulations at the end.

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"
    - "sparse logistic regression learns all discrete pairwise graphical models"
    - "inspectre privately estimating the unseen"
    - "learning graphical models using multiplicative weights"

Connected through hyperedge HE-2 (induced by source paper "causal inference under interference and network uncertainty"):
  with members:
    - "a sound and complete algorithm for learning causal models from relational data"

Connected through hyperedge HE-3 (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"
    - "sparse logistic regression learns all discrete pairwise graphical models"
    - "testing identity of multidimensional histograms"
    - "near optimal closeness testing of discrete histogram distributions"

Connected through hyperedge HE-4 (induced by source paper "on the difficulty of selecting ising models with approximate recovery"):
  with members:
    - "on model misspecification and kl separation for gaussian graphical models"
    - "efficiently learning ising models on arbitrary graphs"
    - "approximate sparsity pattern recovery information theoretic lower bounds"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-5 (induced by source paper "lower bounds on active learning for graphical model selection"):
  with members:
    - "the capacity of adaptive group testing"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-6 (induced by source paper "role discovery in networks"):
  with members:
    - "a boosting approach to learning graph representations"
    - "an information theoretic analysis of hard and soft assignment methods for clustering"
    - "does a daily deal promotion signal a distressed business an empirical investigation of small business survival"
    - "role dynamics fast mining of large dynamic networks"
    - "network sampling from static to streaming graphs"
    - "correlated non parametric latent feature models"
    - "the internet as level topology three data sources and one definitive metric"

Connected through hyperedge HE-7 (induced by source paper "bounded degree approximations of stochastic networks"):
  with members:
    - "the relation between granger causality and directed information theory a review"
    - "on directed information theory and granger causality graphs"

Connected through hyperedge HE-8 (induced by source paper "efficiently learning ising models on arbitrary graphs"):
  with members:
    - "hardness of parameter estimation in graphical models"
    - "analysis of boolean functions"
    - "maximum likelihood bounded tree width markov networks"
    - "moment matching polynomials"
    - "approximation algorithms for two state anti ferromagnetic spin systems on bounded degree graphs"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "computational transition at the uniqueness threshold"
    - "polynomial learning of distribution families"

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)