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 the hardness of losing weight"
  Abstract: We study the complexity of local search for the Boolean constraint satisfaction problem (CSP), in the following form: given a CSP instance, that is, a collection of constraints, and a solution to it, the question is whether there is a better (lighter, i.e., having strictly less Hamming weight) solution within a given distance from the initial solution. We classify the complexity, both classical and parameterized, of such problems by a Schaefer-style dichotomy result, that is, with a restricted set of allowed types of constraints. Our results show that there is a considerable amount of such problems that are NP-hard, but fixed-parameter tractable when parameterized by the distance.

Connected through hyperedge HE-1 (induced by source paper "on the complexity of restoring corrupted colorings"):
  with members:
    - "fine grained complexity analysis of two classic tsp variants"
    - "fixing improper colorings of graphs"
    - "finding shortest paths between graph colourings"
    - "reconfiguration in bounded bandwidth and treedepth"
    - "kernelization lower bounds by cross composition"
    - "adaptive uncertainty resolution in bayesian combinatorial optimization problems"

Connected through hyperedge HE-2 (induced by source paper "kernelization of constraint satisfaction problems a study through universal algebra"):
  with members:
    - "a dichotomy theorem for nonuniform csps"
    - "the proof of csp dichotomy conjecture"
    - "optimal sparsification for some binary csps using low degree polynomials"
    - "sparsification upper and lower bounds for graph problems and not all equal sat"
    - "an effective dichotomy for the counting constraint satisfaction problem"
    - "preprocessing of min ones problems a dichotomy"

Connected through hyperedge HE-3 (induced by source paper "parameterized complexity results for exact bayesian network structure learning"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a transformational characterization of equivalent bayesian network structures"
    - "learning bayesian network structure from massive datasets the sparse candidate algorithm"
    - "a complete anytime algorithm for treewidth"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "algorithms and complexity results for exact bayesian structure learning"
    - "don t be strict in local search"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "constraint satisfaction parameterized by solution size"):
  with members:
    - "kernels for global constraints"
    - "the complexity of global cardinality constraints"
    - "preprocessing of min ones problems a dichotomy"

Connected through hyperedge HE-5 (induced by source paper "don t be strict in local search"):
  with members:
    - "algorithms and complexity results for exact bayesian structure learning"

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)