human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "recognition networks for approximate inference in bn20 networks"):
  Abstract: We propose using recognition networks for approximate inference inBayesian networks (BNs). A recognition network is a multilayerperception (MLP) trained to predict posterior marginals given observedevidence in a particular BN. The input to the MLP is a vector of thestates of the evidential nodes. The activity of an output unit isinterpreted as a prediction of the posterior marginal of thecorresponding variable. The MLP is trained using samples generated fromthe corresponding BN.We evaluate a recognition network that was trained to do inference ina large Bayesian network, similar in structure and complexity to theQuick Medical Reference, Decision Theoretic (QMR-DT). Our networkis a binary, two-layer, noisy-OR network containing over 4000 potentially observable nodes and over 600 unobservable, hidden nodes. Inreal medical diagnosis, most observables are unavailable, and there isa complex and unknown bias that selects which ones are provided. Weincorporate a very basic type of selection bias in our network: a knownpreference that available observables are positive rather than negative.Even this simple bias has a significant effect on the posterior. We compare the performance of our recognition network tostate-of-the-art approximate inference algorithms on a large set oftest cases. In order to evaluate the effect of our simplistic modelof the selection bias, we evaluate algorithms using a variety ofincorrectly modeled observation biases. Recognition networks performwell using both correct and incorrect observation biases.

Includes members:
  Member "a tractable inference algorithm for diagnosing multiple diseases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network")
  Member "loopy belief propagation for approximate inference an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analyzing differentiable fuzzy logic operators")
  Member "ais bn an adaptive importance sampling algorithm for evidential reasoning in large bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "universal marginaliser for deep amortised inference for probabilistic programs")
  Member "variational probabilistic inference and the qmr dt network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the accuracy vs coverage trade off in patient facing diagnosis models")

Question: Please tell me which class the source paper 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)