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: "making sensitivity analysis computationally efficient"
  Abstract: To investigate the robustness of the output probabilities of a Bayesian network, a sensitivity analysis can be performed. A one-way sensitivity analysis establishes, for each of the probability parameters of a network, a function expressing a posterior marginal probability of interest in terms of the parameter. Current methods for computing the coefficients in such a function rely on a large number of network evaluations. In this paper, we present a method that requires just a single outward propagation in a junction tree for establishing the coefficients in the functions for all possible parameters; in addition, an inward propagation is required for processing evidence. Conversely, the method requires a single outward propagation for computing the coefficients in the functions expressing all possible posterior marginals in terms of a single parameter. We extend these results to an n-way sensitivity analysis in which sets of parameters are studied.

Connected through hyperedge HE-1 (induced by source paper "on the relative expressiveness of bayesian and neural networks"):
  with members:
    - "on relaxing determinism in arithmetic circuits"
    - "human level intelligence or animal like abilities"
    - "bounding and counting linear regions of deep neural networks"
    - "a differential approach to inference in bayesian networks"
    - "mean field theory for sigmoid belief networks"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-2 (induced by source paper "a differential approach to inference in bayesian networks"):
  with members:
    - "query dags a practical paradigm for implementing belief network inference"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "sensitivity analysis for probability assessments in bayesian networks"
    - "approximating map using local search"
    - "map complexity results and approximation methods"
    - "when do numbers really matter"
    - "a knowledge compilation map"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-3 (induced by source paper "sensitivity analysis in decision circuits"):
  with members:
    - "sensitivity analysis for probability assessments in bayesian networks"
    - "a differential approach to inference in bayesian networks"
    - "analysing sensitivity data from probabilistic networks"
    - "sensitivity analysis in bayesian networks from single to multiple parameters"
    - "evaluating influence diagrams with decision circuits"
    - "on the robustness of most probable explanations"
    - "when do numbers really matter"

Connected through hyperedge HE-4 (induced by source paper "three new sensitivity analysis methods for influence diagrams"):
  with members:
    - "a measure of decision flexibility"
    - "a differential approach to inference in bayesian networks"
    - "analysing sensitivity data from probabilistic networks"
    - "sensitivity analysis in bayesian networks from single to multiple parameters"
    - "sensitivity analysis in decision circuits"
    - "evaluating influence diagrams with decision circuits"

Connected through hyperedge HE-5 (induced by source paper "sensitivity analysis in bayesian networks from single to multiple parameters"):
  with members:
    - "a logic for reasoning about evidence"
    - "sensitivity analysis for probability assessments in bayesian networks"
    - "a differential approach to inference in bayesian networks"
    - "user interface tools for navigation in conditional probability tables and elicitation of probabilities in bayesian networks"
    - "when do numbers really matter"

Connected through hyperedge HE-6 (induced by source paper "exploiting evidence dependent sensitivity bounds"):
  with members:
    - "sensitivity analysis for probability assessments in bayesian networks"
    - "a differential approach to inference in bayesian networks"
    - "analysing sensitivity data from probabilistic networks"
    - "evidence invariant sensitivity bounds"
    - "when do numbers really matter"

Connected through hyperedge HE-7 (induced by source paper "on the robustness of most probable explanations"):
  with members:
    - "sensitivity analysis for probability assessments in bayesian networks"
    - "a differential approach to inference in bayesian networks"
    - "analysing sensitivity data from probabilistic networks"
    - "sensitivity analysis in bayesian networks from single to multiple parameters"
    - "when do numbers really matter"

Connected through hyperedge HE-8 (induced by source paper "lower bound bayesian networks an efficient inference of lower bounds on probability distributions in bayesian networks"):
  with members:
    - "evidence absorption and propagation through evidence reversals"
    - "interval influence diagrams"
    - "localized partial evaluation of belief networks"
    - "intelligent probabilistic 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)