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: "a sufficiently fast algorithm for finding close to optimal junction trees"
  Abstract: An algorithm is developed for finding a close to optimal junction tree of a given graph G. The algorithm has a worst case complexity O(c^k n^a) where a and c are constants, n is the number of vertices, and k is the size of the largest clique in a junction tree of G in which this size is minimized. The algorithm guarantees that the logarithm of the size of the state space of the heaviest clique in the junction tree produced is less than a constant factor off the optimal value. When k = O(log n), our algorithm yields a polynomial inference algorithm for Bayesian networks.

Connected through hyperedge HE-1 (induced by source paper "a variational approximation for bayesian networks with discrete and continuous latent variables"):
  with members:
    - "approximations in bayesian belief universe for knowledge based systems"
    - "nonuniform dynamic discretization in hybrid networks"
    - "inference using message propagation and topology transformation in vector gaussian continuous networks"
    - "optimal junction trees"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "global conditioning for probabilistic inference in belief networks"
    - "variational probabilistic inference and the qmr dt network"

Connected through hyperedge HE-2 (induced by source paper "topological parameters for time space tradeoff"):
  with members:
    - "graphical models for preference and utility"
    - "a probabilistic approach to hierarchical model based diagnosis"
    - "nested junction trees"
    - "an evaluation of structural parameters for probabilistic reasoning results on benchmark circuits"
    - "optimal junction trees"
    - "global conditioning for probabilistic inference in belief networks"

Connected through hyperedge HE-3 (induced by source paper "efficient approximation for triangulation of minimum treewidth"):
  with members:
    - "optimal decomposition of belief networks"
    - "knowledge engineering for large belief networks"
    - "object oriented bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "on finding minimal w cutset"):
  with members:
    - "an empirical study of w cutset sampling for bayesian networks"
    - "randomized algorithms for the loop cutset problem"

Connected through hyperedge HE-5 (induced by source paper "active tuples based scheme for bounding posterior beliefs"):
  with members:
    - "context specific approximation in probabilistic inference"
    - "large deviation methods for approximate probabilistic inference"
    - "bounded conditioning flexible inference for decisions under scarce resources"
    - "knowledge engineering for large belief networks"
    - "accuracy bounds for belief propagation"
    - "approximate decomposition a method for bounding and estimating probabilistic and deterministic queries"
    - "an empirical study of w cutset sampling for bayesian networks"
    - "a new class of upper bounds on the log partition function"

Connected through hyperedge HE-6 (induced by source paper "exploiting contextual independence in probabilistic inference"):
  with members:
    - "context specific approximation in probabilistic inference"
    - "exploiting the rule structure for decision making within the independent choice logic"
    - "a bayesian approach to learning bayesian networks with local structure"
    - "learning bayesian networks with local structure"
    - "a new look at causal independence"
    - "context specific independence in bayesian networks"
    - "correlated action effects in decision theoretic regression"
    - "mean field theory for sigmoid belief networks"

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.DS(Data Structures and Algorithms)