human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "blankets joint posterior score for learning irregular markov network structures"):
  Abstract: Markov networks are extensively used to model complex sequential, spatial, and relational interactions in a wide range of fields. By learning the structure of independences of a domain, more accurate joint probability distributions can be obtained for inference tasks or, more directly, for interpreting the most significant relations among the variables. However, the performance of current available methods for learning the structure is heavily dependent on the choice of two factors: the structure representation, and the approach for learning such representation. This work follows the probabilistic maximum-a-posteriori approach for learning undirected graph structures, which has gained interest recently. Thus, the Blankets Joint Posterior score is designed for computing the posterior probability of structures given data. In particular, the score proposed can improve the learning process when the solution structure is irregular (that is, when there exists an imbalance in the number of edges over the nodes), which is a property present in many real-world networks. The approximation proposed computes the joint posterior distribution from the collection of Markov blankets of the structure. Essentially, a series of conditional distributions are calculated by using, information about other Markov blankets in the network as evidence. Our experimental results demonstrate that the proposed score has better sample complexity for learning irregular structures, when compared to state-of-the-art scores. By considering optimization with greedy hill-climbing search, we prove for several study cases that our score identifies structures with fewer errors than competitors.

Includes members:
  Member "the libra toolkit for probabilistic models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "eggs a flexible approach to relational modeling of social network spam")
  Member "a survey on independence based markov networks learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blankets joint posterior score for learning markov network structures")
  Member "the ibmap approach for markov network structure learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "log linear models independence structure comparison")
  Member "constrained approximate maximum entropy learning of markov random fields":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blankets joint posterior score for learning markov network structures")

Question: Please tell me which class the source paper 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)