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: "on constrained open world probabilistic databases"
  Abstract: Increasing amounts of available data have led to a heightened need for representing large-scale probabilistic knowledge bases. One approach is to use a probabilistic database, a model with strong assumptions that allow for efficiently answering many interesting queries. Recent work on open-world probabilistic databases strengthens the semantics of these probabilistic databases by discarding the assumption that any information not present in the data must be false. While intuitive, these semantics are not sufficiently precise to give reasonable answers to queries. We propose overcoming these issues by using constraints to restrict this open world. We provide an algorithm for one class of queries, and establish a basic hardness result for another. Finally, we propose an efficient and tight approximation for a large class of queries.

Connected through hyperedge HE-1 (induced by source paper "on constraint definability in tractable probabilistic models"):
  with members:
    - "learning logistic circuits"
    - "on relaxing determinism in arithmetic circuits"
    - "imposing hard constraints on deep networks promises and limitations"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "equality of opportunity in supervised learning"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "adam a method for stochastic optimization"
    - "a differential approach to inference in bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "collaborative policy learning for open knowledge graph reasoning"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "open world knowledge graph completion"
    - "convolutional 2d knowledge graph embeddings"
    - "joint representation learning of text and knowledge for knowledge graph completion"

Connected through hyperedge HE-3 (induced by source paper "mosaic a sample based database system for open world query processing"):
  with members:
    - "approximate query processing using deep generative models"
    - "neo a learned query optimizer"
    - "entropydb a probabilistic approach to approximate query processing"
    - "idebench a benchmark for interactive data exploration"
    - "model based approximate query processing"
    - "generative modeling using the sliced wasserstein distance"
    - "database learning toward a database that becomes smarter every time"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "infinite probabilistic databases"):
  with members:
    - "discrete continuous mixtures in probabilistic programming generalized semantics and inference algorithms"
    - "probabilistic databases with an infinite open world assumption"
    - "aggregation in probabilistic databases via knowledge compilation"
    - "markov logic in infinite domains"
    - "conditioning probabilistic databases"

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)