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: "swift compiled inference for probabilistic programming languages"
  Abstract: A probabilistic program defines a probability measure over its semantic structures. One common goal of probabilistic programming languages (PPLs) is to compute posterior probabilities for arbitrary models and queries, given observed evidence, using a generic inference engine. Most PPL inference engines---even the compiled ones---incur significant runtime interpretation overhead, especially for contingent and open-universe models. This paper describes Swift, a compiler for the BLOG PPL. Swift-generated code incorporates optimizations that eliminate interpretation overhead, maintain dynamic dependencies efficiently, and handle memory management for possible worlds of varying sizes. Experiments comparing Swift with other PPL engines on a variety of inference problems demonstrate speedups ranging from 12x to 326x.

Connected through hyperedge HE-1 (induced by source paper "generative grading neural approximate parsing for automated student feedback"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "reliable deep grade prediction with uncertainty estimation"
    - "automated essay scoring based on two stage learning"
    - "training classifiers with natural language explanations"
    - "multimodal generative models for scalable weakly supervised learning"
    - "zero shot learning for code education rubric sampling with deep learning inference"
    - "attention is all you need"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"

Connected through hyperedge HE-2 (induced by source paper "compositional inference metaprogramming with convergence guarantees"):
  with members:
    - "a convenient category for higher order probability theory"
    - "denotational validation of higher order bayesian inference"
    - "a lambda calculus foundation for universal probabilistic programming"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "church a language for generative models"
    - "gibbs sampling in open universe stochastic languages"
    - "probabilistic operational semantics for the lambda calculus"

Connected through hyperedge HE-3 (induced by source paper "reactive probabilistic programming"):
  with members:
    - "pyro deep universal probabilistic programming"
    - "design and implementation of probabilistic programming language anglican"
    - "c3 lightweight incrementalized mcmc for probabilistic programs using continuations and callsite caching"
    - "expectation propagation for approximate bayesian inference"
    - "rao blackwellised particle filtering for dynamic bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "from high level inference algorithms to efficient code"):
  with members:
    - "autoconj recognizing and exploiting conjugacy without a domain specific language"
    - "exact inference for relational graphical models with interpreted functions lifted probabilistic inference modulo theories"
    - "probabilistic inference modulo theories"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "dynamic blocking and collapsing for gibbs sampling"
    - "church a language for generative models"
    - "exploiting contextual independence in probabilistic inference"
    - "exploiting causal independence in bayesian network inference"

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)