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: "parameter adjustment in bayes networks the generalized noisy or gate"
  Abstract: Spiegelhalter and Lauritzen [15] studied sequential learning in Bayesian networks and proposed three models for the representation of conditional probabilities. A forth model, shown here, assumes that the parameter distribution is given by a product of Gaussian functions and updates them from the _ and _r messages of evidence propagation. We also generalize the noisy OR-gate for multivalued variables, develop the algorithm to compute probability in time proportional to the number of parents (even in networks with loops) and apply the learning model to this gate.

Connected through hyperedge HE-1 (induced by source paper "deepfense online accelerated defense against adversarial deep learning"):
  with members:
    - "magnet a two pronged defense against adversarial examples"
    - "ape gan adversarial perturbation elimination with gan"
    - "magnet and efficient defenses against adversarial attacks are not robust to adversarial examples"
    - "efficient defenses against adversarial attacks"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "defensive distillation is not robust to adversarial examples"
    - "distillation as a defense to adversarial perturbations against deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "conditional probability generation methods for high reliability effects based decision making"):
  with members:
    - "exploiting structure in weighted model counting approaches to probabilistic inference"

Connected through hyperedge HE-3 (induced by source paper "learning bayesian networks with local structure"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "on the sample complexity of learning bayesian networks"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "context specific independence in bayesian networks"
    - "theory refinement on bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "multiplicative factorization of noisy max"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "independence of causal influence and clique tree propagation"
    - "qualitative propagation and scenario based explanation of probabilistic reasoning"
    - "inference with causal independence in the cpsc network"
    - "a generalization of the noisy or model"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-5 (induced by source paper "learning bayesian networks with restricted causal interactions"):
  with members:
    - "context specific approximation in probabilistic inference"
    - "bayesian learning of loglinear models for neural connectivity"
    - "structure and parameter learning for causal independence and causal interaction models"
    - "inter causal independence and heterogeneous factorization"
    - "context specific independence in bayesian networks"
    - "theory refinement on bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "estimating well performing bayesian networks using bernoulli mixtures"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "learning bayesian networks with local structure"
    - "learning bayesian nets that perform well"
    - "mixture approximations to bayesian networks"
    - "being bayesian about network structure"

Connected through hyperedge HE-7 (induced by source paper "using new data to refine a bayesian network"):
  with members:
    - "minimal assumption distribution propagation in belief networks"
    - "using causal information and local measures to learn bayesian networks"
    - "theory refinement on bayesian networks"

Connected through hyperedge HE-8 (induced by source paper "exploiting structure in weighted model counting approaches to probabilistic inference"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "causal independence for knowledge acquisition and inference"
    - "multiplicative factorization of noisy max"
    - "context specific independence in bayesian networks"
    - "exploiting evidence 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)