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: "cp logic a language of causal probabilistic events and its relation to logic programming"
  Abstract: This papers develops a logical language for representing probabilistic causal laws. Our interest in such a language is twofold. First, it can be motivated as a fundamental study of the representation of causal knowledge. Causality has an inherent dynamic aspect, which has been studied at the semantical level by Shafer in his framework of probability trees. In such a dynamic context, where the evolution of a domain over time is considered, the idea of a causal law as something which guides this evolution is quite natural. In our formalization, a set of probabilistic causal laws can be used to represent a class of probability trees in a concise, flexible and modular way. In this way, our work extends Shafer's by offering a convenient logical representation for his semantical objects. #R##N#Second, this language also has relevance for the area of probabilistic logic programming. In particular, we prove that the formal semantics of a theory in our language can be equivalently defined as a probability distribution over the well-founded models of certain logic programs, rendering it formally quite similar to existing languages such as ICL or PRISM. Because we can motivate and explain our language in a completely self-contained way as a representation of probabilistic causal laws, this provides a new way of explaining the intuitions behind such probabilistic logic programs: we can say precisely which knowledge such a program expresses, in terms that are equally understandable by a non-logician. Moreover, we also obtain an additional piece of knowledge representation methodology for probabilistic logic programs, by showing how they can express probabilistic causal laws.

Connected through hyperedge HE-1 (induced by source paper "multiverse causal reasoning using importance sampling in probabilistic programming"):
  with members:
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "universal marginalizer for amortised inference and embedding of generative models"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "pyro deep universal probabilistic programming"
    - "local rule based explanations of black box decision systems"
    - "counterfactual multi agent policy gradients"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "using synthetic data to train neural networks is model based reasoning"

Connected through hyperedge HE-2 (induced by source paper "structure learning of probabilistic logic programs by searching the clause space"):
  with members:
    - "the bayesian structural em algorithm"
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "unachievable region in precision recall space and its effect on empirical evaluation"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "well definedness and efficient inference for probabilistic logic programming under the distribution semantics"
    - "on the implementation of the probabilistic logic programming language problog"
    - "parameter learning of logic programs for symbolic statistical modeling"

Connected through hyperedge HE-3 (induced by source paper "inference and learning in probabilistic logic programs using weighted boolean formulas"):
  with members:
    - "inference in probabilistic logic programs using weighted cnf s"
    - "well definedness and efficient inference for probabilistic logic programming under the distribution semantics"
    - "on the implementation of the probabilistic logic programming language problog"
    - "a knowledge compilation map"
    - "bayesian logic programs"

Connected through hyperedge HE-4 (induced by source paper "combining probabilistic causal and normative reasoning in cp logic"):
  with members:
    - "towards a general framework for actual causation using cp logic"
    - "probabilistic definitions of actual causation using cp logic"
    - "graded causation and defaults"
    - "actual causation in cp logic"

Connected through hyperedge HE-5 (induced by source paper "high level reasoning and low level learning for grasping a probabilistic logic pipeline"):
  with members:
    - "deep learning for detecting robotic grasps"
    - "inference in probabilistic logic programs using weighted cnf s"
    - "planning with noisy probabilistic relational rules"

Connected through hyperedge HE-6 (induced by source paper "fo c a knowledge representation language of causality"):
  with members:
    - "fo c and related modelling paradigms"
    - "probabilistic reasoning with answer sets"

Connected through hyperedge HE-7 (induced by source paper "fo c and related modelling paradigms"):
  with members:
    - "towards a logic based unifying framework for computing"

Connected through hyperedge HE-8 (induced by source paper "negation in the head of cp logic rules"):
  with members:
    - "probabilistic reasoning with answer sets"

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.AI(Artificial Intelligence)