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: "information theoretic properties of markov random fields and their algorithmic applications"
  Abstract: Markov random fields area popular model for high-dimensional probability distributions. Over the years, many mathematical, statistical and algorithmic problems on them have been studied. Until recently, the only known algorithms for provably learning them relied on exhaustive search, correlation decay or various incoherence assumptions. Bresler gave an algorithm for learning general Ising models on bounded degree graphs. His approach was based on a structural result about mutual information in Ising models. #R##N#Here we take a more conceptual approach to proving lower bounds on the mutual information through setting up an appropriate zero-sum game. Our proof generalizes well beyond Ising models, to arbitrary Markov random fields with higher order interactions. As an application, we obtain algorithms for learning Markov random fields on bounded degree graphs on $n$ nodes with $r$-order interactions in $n^r$ time and $\log n$ sample complexity. The sample complexity is information theoretically optimal up to the dependence on the maximum degree. The running time is nearly optimal under standard conjectures about the hardness of learning parity with noise.

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 "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 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"
    - "sparse logistic regression learns all discrete pairwise graphical models"
    - "learning graphical models using multiplicative weights"
    - "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 "regression from dependent observations"):
  with members:
    - "hogwild gibbs can be panaccurate"
    - "learning graphical models using multiplicative weights"
    - "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-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"
    - "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-6 (induced by source paper "sparse logistic regression learns all discrete pairwise graphical models"):
  with members:
    - "learning graphical models using multiplicative weights"
    - "interaction screening efficient and sample optimal learning of ising models"
    - "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:
    - "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-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)