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: "on finding minimal w cutset"
  Abstract: The complexity of a reasoning task over a graphical model is tied to the induced width of the underlying graph. It is well-known that the conditioning (assigning values) on a subset of variables yields a subproblem of the reduced complexity where instantiated variables are removed. If the assigned variables constitute a cycle-cutset, the rest of the network is singly-connected and therefore can be solved by linear propagation algorithms. A w-cutset is a generalization of a cycle-cutset defined as a subset of nodes such that the subgraph with cutset nodes removed has induced-width of w or less. In this paper we address the problem of finding a minimal w-cutset in a graph. We relate the problem to that of finding the minimal w-cutset of a treedecomposition. The latter can be mapped to the well-known set multi-cover problem. This relationship yields a proof of NP-completeness on one hand and a greedy algorithm for finding a w-cutset of a tree decomposition on the other. Empirical evaluation of the algorithms is presented.

Connected through hyperedge HE-1 (induced by source paper "joins via geometric resolutions worst case and beyond"):
  with members:
    - "size bounds and query plans for relational joins"
    - "constraint solving via fractional edge covers"
    - "join processing for graph patterns an old dog with new tricks"
    - "instance optimal geometric algorithms"
    - "beyond worst case analysis for joins with minesweeper"
    - "skew strikes back new developments in the theory of join algorithms"
    - "worst case optimal join algorithms"
    - "tractable hypergraph properties for constraint satisfaction and conjunctive queries"

Connected through hyperedge HE-2 (induced by source paper "modeling transportation routines using hybrid dynamic mixed networks"):
  with members:
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "hybrid processing of beliefs and constraints"
    - "iterative join graph propagation"
    - "approximate inference algorithms for hybrid bayesian networks with discrete constraints"
    - "a simple insight into iterative belief propagation s success"
    - "mixtures of deterministic probabilistic networks and their and or search space"
    - "expectation propogation for approximate inference in dynamic bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "approximate inference algorithms for hybrid bayesian networks with discrete constraints"):
  with members:
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "iterative join graph propagation"
    - "an empirical study of w cutset sampling for bayesian networks"
    - "a simple insight into iterative belief propagation s success"
    - "mixtures of deterministic probabilistic networks and their and or search space"
    - "expectation propogation for approximate inference in dynamic bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "cutset sampling for bayesian networks"):
  with members:
    - "hugs combining exact inference and gibbs sampling in junction trees"
    - "loopy belief propagation for approximate inference an empirical study"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "knowledge engineering for large belief networks"
    - "empirical evaluation of approximation algorithms for probabilistic decoding"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "global conditioning for probabilistic inference in 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)