human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "building large k cores from sparse graphs"):
  Abstract: A popular model to measure network stability is the $k$-core, that is the maximal induced subgraph in which every vertex has degree at least $k$. For example, $k$-cores are commonly used to model the unraveling phenomena in social networks. In this model, users having less than $k$ connections within the network leave it, so the remaining users form exactly the $k$-core. In this paper we study the question whether it is possible to make the network more robust by spending only a limited amount of resources on new connections. A mathematical model for the $k$-core construction problem is the following Edge $k$-Core optimization problem. We are given a graph $G$ and integers $k$, $b$ and $p$. The task is to ensure that the $k$-core of $G$ has at least $p$ vertices by adding at most $b$ edges. #R##N#The previous studies on Edge $k$-Core demonstrate that the problem is computationally challenging. In particular, it is NP-hard when $k=3$, W[1]-hard being parameterized by $k+b+p$ (Chitnis and Talmon, 2018), and APX-hard (Zhou et al, 2019). Nevertheless, we show that there are efficient algorithms with provable guarantee when the $k$-core has to be constructed from a sparse graph with some additional structural properties. Our results are 1) When the input graph is a forest, Edge $k$-Core is solvable in polynomial time; 2) Edge $k$-Core is fixed-parameter tractable (FPT) being parameterized by the minimum size of a vertex cover in the input graph. On the other hand, with such parameterization, the problem does not admit a polynomial kernel subject to a widely-believed assumption from complexity theory; 3) Edge $k$-Core is FPT parameterized by $\mathrm{tw}+k$. This improves upon the result of Chitnis and Talmon by not requiring $b$ to be small. Each of our algorithms is built upon a new graph-theoretical result interesting in its own.

Includes members:
  Member "a survey of parameterized algorithms and the complexity of edge modification":
    also connected through other hyperedges:
      (none)
  Member "finding even subgraphs even faster":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the complexity of finding large odd induced subgraphs and odd colorings")
  Member "editing to a connected graph of given degrees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph editing to a given degree sequence")
  Member "preventing unraveling in social networks gets harder":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "k core minimization a game theoretic approach")
  Member "parameterized complexity of the anchored k core problem for directed graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a parameterized complexity view on collapsing k cores")
  Member "editing to a graph of given degrees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph editing to a given degree sequence")
  Member "understanding edge connectivity in the internet through core decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient core maintenance in large dynamic graphs")
  Member "k core decomposition of internet graphs hierarchies self similarity and measurement biases":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "anatomy of an online misinformation network")

Question: Please tell me which class the source paper 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)