human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "structure learning of probabilistic logic programs by searching the clause space"
  Abstract: Learning probabilistic logic programming languages is receiving an increasing attention, and systems are available for learning the parameters (PRISM, LeProbLog, LFI-ProbLog and EMBLEM) or both structure and parameters (SEM-CP-logic and SLIPCASE) of these languages. In this paper we present the algorithm SLIPCOVER for “Structure LearnIng of Probabilistic logic programs by searChing OVER the clause space.” It performs a beam search in the space of probabilistic clauses and a greedy search in the space of theories using the log likelihood of the data as the guiding heuristics. To estimate the log likelihood, SLIPCOVER performs Expectation Maximization with EMBLEM. The algorithm has been tested on five real world datasets and compared with SLIPCASE, SEM-CP-logic, Aleph and two algorithms for learning Markov Logic Networks (Learning using Structural Motifs (LSM) and ALEPH++ExactL1). SLIPCOVER achieves higher areas under the precision-recall and receiver operating characteristic curves in most cases.

Connected through hyperedge HE-1 (induced by source paper "interpretable multiclass classification by mdl based rule lists"):
  with members:
    - "scalable bayesian rule lists"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-2 (induced by source paper "inductive general game playing"):
  with members:
    - "inductive learning of answer set programs from noisy examples"
    - "exploiting answer set programming with external sources for meta interpretive learning"
    - "learning explanatory rules from noisy data"
    - "learning first order definable concepts over structures of small degree"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "iterative learning of answer set programs from context dependent examples"
    - "learning weak constraints in answer set programming"
    - "clingo asp control preliminary report"

Connected through hyperedge HE-3 (induced by source paper "inductive logic programming via differentiable deep neural logic networks"):
  with members:
    - "induction of non monotonic logic programs to explain boosted tree models using lime"
    - "structure learning for relational logistic regression an ensemble approach"
    - "learning explanatory rules from noisy data"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "using swish to realise interactive web based tutorials for logic based languages"):
  with members:
    - "programming in logic without logic programming"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "pengines web logic programming made easy"
    - "well definedness and efficient inference for probabilistic logic programming under the distribution semantics"
    - "swi prolog and the web"
    - "pldoc wiki style literate programming for prolog"
    - "extending classical logic with inductive definitions"

Question: Please tell me which class the center node 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)