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: "secluded connectivity problems"
  Abstract: Consider a setting where possibly sensitive information sent over a path in a network is visible to every {neighbor} of the path, i.e., every neighbor of some node on the path, thus including the nodes on the path itself. The exposure of a path $P$ can be measured as the number of nodes adjacent to it, denoted by $N[P]$. A path is said to be secluded if its exposure is small. A similar measure can be applied to other connected subgraphs, such as Steiner trees connecting a given set of terminals. Such subgraphs may be relevant due to considerations of privacy, security or revenue maximization. This paper considers problems related to minimum exposure connectivity structures such as paths and Steiner trees. It is shown that on unweighted undirected $n$-node graphs, the problem of finding the minimum exposure path connecting a given pair of vertices is strongly inapproximable, i.e., hard to approximate within a factor of $O(2^{\log^{1-\epsilon}n})$ for any $\epsilon>0$ (under an appropriate complexity assumption), but is approximable with ratio $\sqrt{\Delta}+3$, where $\Delta$ is the maximum degree in the graph. One of our main results concerns the class of bounded-degree graphs, which is shown to exhibit the following interesting dichotomy. On the one hand, the minimum exposure path problem is NP-hard on node-weighted or directed bounded-degree graphs (even when the maximum degree is 4). On the other hand, we present a polynomial algorithm (based on a nontrivial dynamic program) for the problem on unweighted undirected bounded-degree graphs. Likewise, the problem is shown to be polynomial also for the class of (weighted or unweighted) bounded-treewidth graphs.

Connected through hyperedge HE-1 (induced by source paper "parameterized algorithms and data reduction for the short secluded s t path problem"):
  with members:
    - "on the computational complexity of length and neighborhood constrained path problems"
    - "the parameterized complexity of finding secluded solutions to some classical optimization problems on graphs"
    - "a parameterized approximation algorithm for the mixed and windy capacitated arc routing problem theory and experiments"
    - "finding connected secluded subgraphs"
    - "polynomial kernels for weighted problems"
    - "parameterized complexity of secluded connectivity problems"
    - "route planning in transportation networks"
    - "parameterized complexity of the k arc chinese postman problem"

Connected through hyperedge HE-2 (induced by source paper "enumerating isolated cliques in temporal networks"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "listing all maximal k plexes in temporal graphs"
    - "the parameterized complexity of finding secluded solutions to some classical optimization problems on graphs"
    - "enumerating maximal cliques in link streams with durations"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "adapting the bron kerbosch algorithm for enumerating maximal cliques in temporal graphs"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "computing maximal cliques in link streams"

Connected through hyperedge HE-3 (induced by source paper "the parameterized complexity of finding secluded solutions to some classical optimization problems on graphs"):
  with members:
    - "finding secluded places of special interest in graphs"
    - "a shortcut to sun flowers kernels in logarithmic space or linear time"
    - "parameterized complexity of secluded connectivity problems"
    - "towards optimal and expressive kernelization for d hitting set"
    - "on the parameterized complexity of cutting a few vertices from a graph"
    - "approximation and tidying a problem kernel for s plex cluster vertex deletion"
    - "finding small separators in linear time via treewidth reduction"

Connected through hyperedge HE-4 (induced by source paper "on the computational complexity of length and neighborhood constrained path problems"):
  with members:
    - "hardness results and approximation algorithms for the minimum dominating tree problem"
    - "the parameterized complexity of finding secluded solutions to some classical optimization problems on graphs"
    - "finding connected secluded subgraphs"
    - "parameterized complexity of secluded connectivity problems"
    - "kernelization lower bounds by cross composition"

Connected through hyperedge HE-5 (induced by source paper "finding secluded places of special interest in graphs"):
  with members:
    - "parameterized complexity of secluded connectivity problems"
    - "towards optimal and expressive kernelization for d hitting set"
    - "on the parameterized complexity of cutting a few vertices from a graph"
    - "approximation and tidying a problem kernel for s plex cluster vertex deletion"
    - "finding small separators in linear time via treewidth reduction"

Connected through hyperedge HE-6 (induced by source paper "parameterized complexity of secluded connectivity problems"):
  with members:
    - "kernelization lower bounds by cross composition"
    - "solving connectivity problems parameterized by treewidth in single exponential time"
    - "kernels for below upper bound parameterizations of the hitting set and directed dominating set problems"
    - "treewidth computation and extremal combinatorics"

Connected through hyperedge HE-7 (induced by source paper "the thinnest path problem"):
  with members:
    - "dynamic shortest path algorithms for hypergraphs"

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)