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: "interaction screening efficient and sample optimal learning of ising models"
  Abstract: We consider the problem of learning the underlying graph of an unknown Ising model on p spins from a collection of i.i.d. samples generated from the model. We suggest a new estimator that is computationally efficient and requires a number of samples that is near-optimal with respect to previously established information-theoretic lower-bound. Our statistical estimator has a physical interpretation in terms of "interaction screening". The estimator is consistent and is efficiently implemented using convex optimization. We prove that with appropriate regularization, the estimator recovers the underlying graph using a number of samples that is logarithmic in the system size p and exponential in the maximum coupling-intensity and maximum node-degree.

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 "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"
    - "learning without concentration"
    - "efficiently learning ising models on arbitrary graphs"
    - "towards a mathematical theory of super resolution"
    - "subset selection for gaussian markov random fields"

Connected through hyperedge HE-3 (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"
    - "efficiently learning ising models on arbitrary graphs"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-4 (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"
    - "efficiently learning ising models on arbitrary graphs"

Connected through hyperedge HE-5 (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"
    - "hardness of parameter estimation in graphical models"
    - "efficiently learning ising models on arbitrary graphs"

Connected through hyperedge HE-6 (induced by source paper "sparse logistic regression learns all discrete pairwise graphical models"):
  with members:
    - "learning graphical models using multiplicative weights"
    - "information theoretic properties of markov random fields and their algorithmic applications"
    - "efficiently learning ising models on arbitrary graphs"
    - "regularization techniques for learning with matrices"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-7 (induced by source paper "learning graphical models using multiplicative weights"):
  with members:
    - "information theoretic properties of markov random fields and their algorithmic applications"
    - "efficiently learning ising models on arbitrary graphs"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-8 (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"

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)