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: "learning graphical models using multiplicative weights"
  Abstract: We give a simple, multiplicative-weight update algorithm for learning undirected graphical models or Markov random fields (MRFs). The approach is new, and for the well-studied case of Ising models or Boltzmann machines, we obtain an algorithm that uses a nearly optimal number of samples and has quadratic running time (up to logarithmic factors), subsuming and improving on all prior work. Additionally, we give the first efficient algorithm for learning Ising models over general alphabets. #R##N#Our main application is an algorithm for learning the structure of t-wise MRFs with nearly-optimal sample complexity (up to polynomial losses in necessary terms that depend on the weights) and running time that is $n^{O(t)}$. In addition, given $n^{O(t)}$ samples, we can also learn the parameters of the model and generate a hypothesis that is close in statistical distance to the true MRF. All prior work runs in time $n^{\Omega(d)}$ for graphs of bounded degree d and does not generate a hypothesis close in statistical distance even for t=3. We observe that our runtime has the correct dependence on n and t assuming the hardness of learning sparse parities with noise. #R##N#Our algorithm--the Sparsitron-- is easy to implement (has only one parameter) and holds in the on-line setting. Its analysis applies a regret bound from Freund and Schapire's classic Hedge algorithm. It also gives the first solution to the problem of learning sparse Generalized Linear Models (GLMs).

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"
    - "differentially private learning of undirected graphical models using collective graphical models"

Connected through hyperedge HE-2 (induced by source paper "efficient distance approximation for structured high dimensional distributions via learning"):
  with members:
    - "efficiently learning and sampling interventional distributions from observations"
    - "learning and testing causal models with interventions"
    - "testing ising models"
    - "square hellinger subadditivity for bayesian networks and its applications to identity testing"
    - "testing bayesian networks"
    - "causal networks semantics and expressiveness"
    - "optimal algorithms for testing closeness of discrete distributions"
    - "testing probability distributions using conditional samples"

Connected through hyperedge HE-3 (induced by source paper "lower bounds for testing graphical models colorings and antiferromagnetic ising models"):
  with members:
    - "approximating pairwise correlations in the ising model"
    - "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"
    - "random cluster dynamics for the ising model is rapidly mixing"

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"
    - "information theoretic optimal learning of gaussian graphical models"
    - "interaction screening efficient and sample optimal learning of ising 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-5 (induced by source paper "efficient learning of discrete graphical models"):
  with members:
    - "sparse logistic regression learns all discrete pairwise graphical models"
    - "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-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"
    - "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-7 (induced by source paper "regression from dependent observations"):
  with members:
    - "hogwild gibbs can be panaccurate"
    - "information theoretic properties of markov random fields and their algorithmic applications"
    - "testing ising models"
    - "ensuring rapid mixing and low bias for asynchronous gibbs sampling"
    - "social contagion theory examining dynamic social networks and human behavior"

Connected through hyperedge HE-8 (induced by source paper "learning ising models with independent failures"):
  with members:
    - "sparse logistic regression learns all discrete pairwise graphical models"
    - "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"

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)