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: "relational marginal problems theory and estimation"
  Abstract: In the propositional setting, the marginal problem is to find a (maximum-entropy) distribution that has some given marginals. We study this problem in a relational setting and make the following contributions. First, we compare two different notions of relational marginals. Second, we show a duality between the resulting relational marginal problems and the maximum likelihood estimation of the parameters of relational models, which generalizes a well-known duality from the propositional setting. Third, by exploiting the relational marginal formulation, we present a statistically sound method to learn the parameters of relational models that will be applied in settings where the number of constants differs between the training and test data. Furthermore, based on a relational generalization of marginal polytopes, we characterize cases where the standard estimators based on feature's number of true groundings needs to be adjusted and we quantitatively characterize the consequences of these adjustments. Fourth, we prove bounds on expected errors of the estimated parameters, which allows us to lower-bound, among other things, the effective sample size of relational training data.

Connected through hyperedge HE-1 (induced by source paper "domain liftability of relational marginal polytopes"):
  with members:
    - "hardness of parameter estimation in graphical models"
    - "skolemization for weighted first order model counting"

Connected through hyperedge HE-2 (induced by source paper "neural markov logic networks"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "differentiable reasoning on large knowledge bases and natural language"
    - "lyrics a general interface layer to integrate ai and deep learning"
    - "deepproblog neural probabilistic logic programming"
    - "inference learning and population size projectivity for srl models"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "lifted weight learning of markov logic networks revisited"):
  with members:
    - "skolemization for weighted first order model counting"
    - "entropy optimization and counting"

Connected through hyperedge HE-4 (induced by source paper "vc dimension based generalization bounds for relational learning"):
  with members:
    - "end to end differentiable proving"
    - "induction of interpretable possibilistic logic theories from relational data"
    - "lifted relational neural networks"

Connected through hyperedge HE-5 (induced by source paper "pac reasoning in relational domains"):
  with members:
    - "end to end differentiable proving"
    - "induction of interpretable possibilistic logic theories from relational data"

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)