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: "efficiently enumerating hitting sets of hypergraphs arising in data profiling"
  Abstract: We devise a method to enumerate the inclusion-wise minimal hitting sets of a hypergraph. The algorithm has delay $O( m^{k^*+1} \, n^2)$ on $n$-vertex, $m$-edge hypergraphs, where $k^*$ is the rank of the transversal hypergraph, i.e., the cardinality of the largest minimal solution. In particular, on classes of hypergraphs for which $k^*$ is bounded, the delay is polynomial. The algorithm uses space linear in the input size only. The enumeration methods solves the extension problem for minimal hitting sets as a subroutine. We show that this problem, parameterised by the cardinality of the set which is to be extended, is one of the first natural W[3]-complete problems. We give an algorithm for the subroutine that is optimal under the assumption that $W[2] \neq \mathrm{FPT}$ or the exponential time hypothesis (ETH), respectively. Despite the hardness of the extension problem, we provide empirical evidence indicating that the enumeration outperforms its theoretical worst-case guarantee on hypergraphs arising in the profiling of relational databases, namely, in the detection of unique column combinations. Our analysis suggest that these hypergraphs exhibit structure that allows the subroutine to be fast on average.

Connected through hyperedge HE-1 (induced by source paper "optimal size problem kernels for d hitting set in linear time and space"):
  with members:
    - "understanding the effectiveness of data reduction in public transportation networks"
    - "on approximate data reduction for the rural postman problem theory and experiments"
    - "computing hitting set kernels by ac 0 circuits"
    - "exploiting hidden structure in selecting dimensions that distinguish vectors"
    - "a shortcut to sun flowers kernels in logarithmic space or linear time"
    - "towards optimal and expressive kernelization for d hitting set"

Connected through hyperedge HE-2 (induced by source paper "the minimization of random hypergraphs"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"

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)