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 limits of selecting binary graphical models in high dimensions"
  Abstract: The problem of graphical model selection is to correctly estimate the graph structure of a Markov random field given samples from the underlying distribution. We analyze the information-theoretic limitations of the problem of graph selection for binary Markov random fields under high-dimensional scaling, in which the graph size $p$ and the number of edges $k$, and/or the maximal node degree $d$ are allowed to increase to infinity as a function of the sample size $n$. For pairwise binary Markov random fields, we derive both necessary and sufficient conditions for correct graph selection over the class $\mathcal{G}_{p,k}$ of graphs on $p$ vertices with at most $k$ edges, and over the class $\mathcal{G}_{p,d}$ of graphs on $p$ vertices with maximum degree at most $d$. For the class $\mathcal{G}_{p, k}$, we establish the existence of constants $c$ and $c'$ such that if $\numobs   c' k^2 \log p$. Similarly, for the class $\mathcal{G}_{p,d}$, we exhibit constants $c$ and $c'$ such that for $n   c' d^3 \log p$.

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 "the sample complexity of meta sparse regression"):
  with members:
    - "an introductory guide to fano s inequality with applications in statistical estimation"
    - "online meta learning"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"
    - "meta learning a survey"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "matching networks for one shot learning"
    - "on the information theoretic limits of learning ising models"

Connected through hyperedge HE-3 (induced by source paper "first order methods take exponential time to converge to global minimizers of non convex functions"):
  with members:
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-4 (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-5 (induced by source paper "an introductory guide to fano s inequality with applications in statistical estimation"):
  with members:
    - "a strong converse bound for multiple hypothesis testing with applications to high dimensional estimation"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "worst case vs average case design for estimation from fixed pairwise comparisons"
    - "an adaptive sublinear time block sparse fourier transform"
    - "lower bounds on active learning for graphical model selection"
    - "f divergence inequalities"
    - "on the difficulty of selecting ising models with approximate recovery"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"

Connected through hyperedge HE-6 (induced by source paper "learning graphs from linear measurements fundamental trade offs and applications"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "failure localization in power systems via tree partitions"
    - "learning identifiable gaussian bayesian networks in polynomial time and sample complexity"
    - "exact topology and parameter estimation in distribution grids with minimal observability"
    - "patopa a data driven parameter and topology joint estimation framework in distribution grids"
    - "learning linear structural equation models in polynomial time and sample complexity"
    - "information theoretic limits of bayesian network structure learning"
    - "learning sparse graphs under smoothness prior"

Connected through hyperedge HE-7 (induced by source paper "minimax bounds for structured prediction"):
  with members:
    - "learning maximum a posteriori perturbation models for structured prediction in polynomial time"
    - "learning latent variable structured prediction models with gaussian perturbations"
    - "on the information theoretic limits of learning ising models"
    - "grammar as a foreign language"
    - "show and tell a neural image caption generator"
    - "multiclass learnability and the erm principle"
    - "search based structured prediction"

Connected through hyperedge HE-8 (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"
    - "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.IT(Information Theory)