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 markov network structures"):
  Abstract: Abstract   Markov networks are extensively used to model complex sequential, spatial, and relational interactions in a wide range of fields. By learning the Markov network independence structure 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. Recently, several researchers have investigated techniques for automatically learning the structure from data by obtaining the probabilistic maximum-a-posteriori structure given the available data. However, all the approximations proposed decompose the posterior of the whole structure into local sub-problems, by assuming that the posteriors of the Markov blankets of all the variables are mutually independent. In this work, we propose a scoring function for relaxing such assumption. The  Blankets Joint Posterior  score computes the joint posterior of structures as a joint distribution of the collection of its Markov blankets. Essentially, the whole posterior is obtained by computing the posterior of the blanket of each variable as a conditional distribution that takes into account information from other blankets in the network. We show in our experimental results that the proposed approximation can improve the sample complexity of state-of-the-art competitors when learning complex networks, where the independence assumption between blanket variables is clearly incorrect.

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 irregular 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 irregular markov network structures")
  Member "efficiently inducing features of conditional random fields":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "geometric matrix completion with deep conditional random fields")

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)