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: "a survey on independence based markov networks learning"
  Abstract: The problem of learning the Markov network structure from data has become increasingly important in machine learning, and in many other application fields. Markov networks are probabilistic graphical models, a widely used formalism for handling probability distributions in intelligent systems. This document focuses on a technology called independence-based learning, which allows for the learning of the independence structure of Markov networks from data in an efficient and sound manner, whenever the dataset is sufficiently large, and data is a representative sample of the target distribution. In the analysis of such technology, this work surveys the current state-of-the-art algorithms, discussing its limitations, and posing a series of open problems where future work may produce some advances in the area, in terms of quality and efficiency.

Connected through hyperedge HE-1 (induced by source paper "blankets joint posterior score for learning markov network structures"):
  with members:
    - "the libra toolkit for probabilistic models"
    - "the ibmap approach for markov network structure learning"
    - "constrained approximate maximum entropy learning of markov random fields"
    - "efficiently inducing features of conditional random fields"

Connected through hyperedge HE-2 (induced by source paper "blankets joint posterior score for learning irregular markov network structures"):
  with members:
    - "the libra toolkit for probabilistic models"
    - "the ibmap approach for markov network structure learning"
    - "constrained approximate maximum entropy learning of markov random fields"

Connected through hyperedge HE-3 (induced by source paper "the ibmap approach for markov network structure learning"):
  with members:
    - "constrained approximate maximum entropy learning of markov random fields"
    - "efficiently inducing features of conditional random fields"

Connected through hyperedge HE-4 (induced by source paper "discovering the markov network structure"):
  with members:
    - "a submodular supermodular procedure with applications to discriminative structure learning"

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.AI(Artificial Intelligence)