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 "learning selectively conditioned forest structures with applications to dbns and classification"):
  Abstract: Dealing with uncertainty in Bayesian Network structures using maximum a posteriori (MAP) estimation or Bayesian Model Averaging (BMA) is often intractable due to the superexponential number of possible directed, acyclic graphs. When the prior is decomposable, two classes of graphs where efficient learning can take place are tree structures, and fixed-orderings with limited in-degree. We show how MAP estimates and BMA for selectively conditioned forests (SCF), a combination of these two classes, can be computed efficiently for ordered sets of variables. We apply SCFs to temporal data to learn Dynamic Bayesian Networks having an intra-timestep forest and inter-timestep limited in-degree structure, improving model accuracy over DBNs without the combination of structures. We also apply SCFs to Bayes Net classification to learn selective forest augmented Naive Bayes classifiers. We argue that the built-in feature selection of selective augmented Bayes classifiers makes them preferable to similar non-selective classifiers based on empirical evidence.

Includes members:
  Member "tractable bayesian learning of tree belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finding all bayesian network structures within a factor of optimal")
  Member "learning the structure of dynamic probabilistic networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on open universe causal reasoning")
  Member "learning bayesian networks the combination of knowledge and statistical data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a tutorial on learning with bayesian networks")
  Member "induction of selective bayesian classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fraud uncollectible debt detection using a bayesian network based learning system a rare binary outcome with mixed data structures")
  Member "theory refinement on bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a tutorial on learning with bayesian networks")
  Member "being bayesian about network structure":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bayesian causal inference via probabilistic program synthesis")
  Member "conditional chow liu tree structures for modeling discrete valued vector time series":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "to frontalize or not to frontalize a study of face pre processing techniques and their impact on recognition")

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.LG(Machine Learning)