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 "scaling inference for markov logic with a task decomposition approach"):
  Abstract: Motivated by applications in large-scale knowledge base construction, we study the problem of scaling up a sophisticated statistical inference framework called Markov Logic Networks (MLNs). Our approach, Felix, uses the idea of Lagrangian relaxation from mathematical programming to decompose a program into smaller tasks while preserving the joint-inference property of the original MLN. The advantage is that we can use highly scalable specialized algorithms for common tasks such as classication and coreference. We propose an architecture to support Lagrangian relaxation in an RDBMS which we show enables scalable joint inference for MLNs. We empirically validate that Felix is signicantly more scalable and ecient than prior approaches to MLN inference by constructing a knowledge base from 1.8M documents as part of the TAC challenge. We show that Felix scales and achieves state-of-the-art quality numbers. In contrast, prior approaches do not scale even to a subset of the corpus that is three orders of magnitude smaller.

Includes members:
  Member "improving the accuracy and efficiency of map inference for markov logic":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond markov logic efficient mining of prediction rules in large graphs")
  Member "tuffy scaling up statistical inference in markov logic networks using an rdbms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonlinear equation solving a faster alternative to feedforward computation")
  Member "scalable probabilistic databases with factor graphs and mcmc":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint reasoning for multi faceted commonsense knowledge")
  Member "lagrangian relaxation for map estimation in graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "map inference via l2 sphere linear program reformulation")

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)