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 "how fast can we reach a target vertex in stochastic temporal graphs"):
  Abstract: Temporal graphs are used to abstractly model real-life networks that are inherently dynamic in nature. Given a static underlying graph $G=(V,E)$, a temporal graph on $G$ is a sequence of snapshots $G_t$, one for each time step $t\geq 1$. In this paper we study stochastic temporal graphs, i.e. stochastic processes $\mathcal{G}$ whose random variables are the snapshots of a temporal graph on $G$. A natural feature observed in various real-life scenarios is a memory effect in the appearance probabilities of particular edges; i.e. the probability an edge $e\in E$ appears at time step $t$ depends on its appearance (or absence) at the previous $k$ steps. In this paper we study the hierarchy of models memory-$k$, addressing this memory effect in an edge-centric network evolution: every edge of $G$ has its own independent probability distribution for its appearance over time. Clearly, for every $k\geq 1$, memory-$(k-1)$ is a special case of memory-$k$. We make a clear distinction between the values $k=0$ ("no memory") and $k\geq 1$ ("some memory"), as in some cases these models exhibit a fundamentally different computational behavior, as our results indicate. For every $k\geq 0$ we investigate the complexity of two naturally related, but fundamentally different, temporal path (journey) problems: MINIMUM ARRIVAL and BEST POLICY. In the first problem we are looking for the expected arrival time of a foremost journey between two designated vertices $s,y$. In the second one we are looking for the arrival time of the best policy for actually choosing a particular $s$-$y$ journey. We present a detailed investigation of the computational landscape of both problems for the different values of memory $k$. Among other results we prove that, surprisingly, MINIMUM ARRIVAL is strictly harder than BEST POLICY; in fact, for $k=0$, MINIMUM ARRIVAL is #P-hard while BEST POLICY is solvable in $O(n^2)$ time.

Includes members:
  Member "temporal vertex cover with a sliding time window":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating multistage matching problems")
  Member "deleting edges to restrict the size of an epidemic in temporal networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed computation and reconfiguration in actively dynamic networks")
  Member "sliding window temporal graph coloring":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "computing maximum matchings in temporal graphs")
  Member "cover time in edge uniform stochastically evolving graphs":
    also connected through other hyperedges:
      (none)
  Member "adapting the bron kerbosch algorithm for enumerating maximal cliques in temporal graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "computing maximum matchings in temporal graphs")
  Member "computing maximal cliques in link streams":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "span core decomposition for temporal networks algorithms and applications")
  Member "temporal network optimization subject to connectivity constraints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed computation and reconfiguration in actively dynamic networks")
  Member "on temporal graph exploration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "live exploration with mobile robots in a dynamic ring revisited")

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.CC(Computational Complexity)