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: "on the information theoretic limits of learning ising models"
  Abstract: We provide a general framework for computing lower-bounds on the sample complexity of recovering the underlying graphs of Ising models, given i.i.d samples. While there have been recent results for specific graph classes, these involve fairly extensive technical arguments that are specialized to each specific graph class. In contrast, we isolate two key graph-structural ingredients that can then be used to specify sample complexity lower-bounds. Presence of these structural properties makes the graph class hard to learn. We derive corollaries of our main result that not only recover existing recent results, but also provide lower bounds for novel graph classes not considered previously. We also extend our framework to the random graph setting and derive corollaries for Erd\H{o}s-R\'{e}nyi graphs in a certain dense setting.

Connected through hyperedge HE-1 (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"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-2 (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"
    - "grammar as a foreign language"
    - "show and tell a neural image caption generator"
    - "multiclass learnability and the erm principle"
    - "search based structured prediction"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-3 (induced by source paper "lower bounds for two sample structural change detection in ising and gaussian models"):
  with members:
    - "testing ising models"
    - "efficiently learning ising models on arbitrary graphs"
    - "learning structural changes of gaussian graphical models in controlled experiments"
    - "high dimensional gaussian graphical model selection walk summability and local separation criterion"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

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)