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: "efficiently learning ising models on arbitrary graphs"
  Abstract: We consider the problem of reconstructing the graph underlying an Ising model from i.i.d. samples. Over the last fifteen years this problem has been of significant interest in the statistics, machine learning, and statistical physics communities, and much of the effort has been directed towards finding algorithms with low computational cost for various restricted classes of models. Nevertheless, for learning Ising models on general graphs with $p$ nodes of degree at most $d$, it is not known whether or not it is possible to improve upon the $p^{d}$ computation needed to exhaustively search over all possible neighborhoods for each node. #R##N#In this paper we show that a simple greedy procedure allows to learn the structure of an Ising model on an arbitrary bounded-degree graph in time on the order of $p^2$. We make no assumptions on the parameters except what is necessary for identifiability of the model, and in particular the results hold at low-temperatures as well as for highly non-uniform models. The proof rests on a new structural property of Ising models: we show that for any node there exists at least one neighbor with which it has a high mutual information. This structural property may be of independent interest.

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 "lower bounds for testing graphical models colorings and antiferromagnetic ising models"):
  with members:
    - "approximating pairwise correlations in the ising model"
    - "learning graphical models using multiplicative weights"
    - "sample optimal identity testing with high probability"
    - "structure learning of h colorings"
    - "a new approach for testing properties of discrete distributions"
    - "testing ising models"
    - "bis hardness for 2 spin systems on bipartite bounded degree graphs in the tree non uniqueness region"
    - "inapproximability of the partition function for the antiferromagnetic ising and hard core models"

Connected through hyperedge HE-3 (induced by source paper "topology inference over networks with nonlinear coupling"):
  with members:
    - "consistent tomography under partial observations over adaptive networks"
    - "signal processing on graphs causal modeling of un structured data"
    - "linear identification of nonlinear systems a lifting technique based on the koopman operator"
    - "social learning over weakly connected graphs"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "distributed detection over adaptive networks refined asymptotics and the role of connectivity"
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "directed information graphs"

Connected through hyperedge HE-4 (induced by source paper "learning some popular gaussian graphical models without condition number bounds"):
  with members:
    - "reducibility and computational lower bounds for problems with planted sparse structure"
    - "learning graphical models using multiplicative weights"
    - "information theoretic optimal learning of gaussian graphical models"
    - "interaction screening efficient and sample optimal learning of ising models"
    - "learning without concentration"
    - "towards a mathematical theory of super resolution"
    - "subset selection for gaussian markov random fields"

Connected through hyperedge HE-5 (induced by source paper "efficient learning of discrete graphical models"):
  with members:
    - "sparse logistic regression learns all discrete pairwise graphical models"
    - "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"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-6 (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"
    - "sparse logistic regression learns all discrete pairwise graphical models"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "learning ising models with independent failures"):
  with members:
    - "sparse logistic regression learns all discrete pairwise graphical models"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "on resampling vs adjusting probabilistic graphical models in estimation of distribution algorithms"):
  with members:
    - "on the performance of multi objective estimation of distribution algorithms for combinatorial problems"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "information theoretic limits of selecting binary graphical models in high dimensions"
    - "analysis of estimation of distribution algorithms and genetic algorithms on nk landscapes"

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)