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 "contrastive feature induction for efficient structure learning of conditional random fields"):
  Abstract: Structure learning of Conditional Random Fields (CRFs) can be cast into an L1-regularized optimization problem. To avoid optimizing over a fully linked model, gain-based or gradient-based feature selection methods start from an empty model and incrementally add top ranked features to it. However, for high-dimensional problems like statistical relational learning, training time of these incremental methods can be dominated by the cost of evaluating the gain or gradient of a large collection of candidate features. In this study we propose a fast feature evaluation algorithm called Contrastive Feature Induction (CFI), which only evaluates a subset of features that involve both variables with high signals (deviation from mean) and variables with high errors (residue). We prove that the gradient of candidate features can be represented solely as a function of signals and errors, and that CFI is an efficient approximation of gradient-based evaluation methods. Experiments on synthetic and real data sets show competitive learning speed and accuracy of CFI on pairwise CRFs, compared to state-of-the-art structure learning methods such as full optimization over all features, and Grafting.

Includes members:
  Member "loopy belief propagation for approximate inference an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analyzing differentiable fuzzy logic operators")
  Member "discriminative probabilistic models for relational data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning distributional programs for relational autocompletion")
  Member "template based inference in symmetric relational markov random fields":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "state space abstractions for probabilistic inference a systematic review")
  Member "a generalized mean field algorithm for variational inference in exponential families":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "variational template machine for data to text generation")
  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.LG(Machine Learning)