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: "the graph exploration problem with advice"
  Abstract: Moving an autonomous agent through an unknown environment is one of the crucial problems for robotics and network analysis. Therefore, it received a lot of attention in the last decades and was analyzed in many different settings. The graph exploration problem is a theoretical and abstract model, where an algorithm has to decide how the agent, also called explorer, moves through a network such that every point of interest is visited at least once. For its decisions, the knowledge of the algorithm is limited by the perception of the explorer. #R##N#There are different models regarding the perception of the explorer. We look at the fixed graph scenario proposed by Kalyanasundaram and Pruhs (Proc. of ICALP, 1993), where the explorer starts at a vertex of the network and sees all reachable vertices, their unique names and their distance from the current position. Therefore, the algorithm recognizes already seen vertices and can adapt its strategy during exploring, because it does not forget anything. #R##N#Because the algorithm only learns the structure of the graph during computation, it cannot deterministically compute an optimal tour that visits every vertex at least once without prior knowledge. Therefore, we are interested in the amount of crucial a-priori information needed to solve the problem optimally, which we measure in terms of the well-studied model of advice complexity. [..] #R##N#We look at different variations of the graph exploration problem and distinguish between directed or undirected edges, cyclic or non-cyclic solutions, unit costs or individual costs for the edges and different amounts of a-priori structural knowledge of the explorer. [..] In this work, we present algorithms with an advice complexity of $\mathcal{O}(m+n)$, thus improving the classical bound for sparse graphs.

Connected through hyperedge HE-1 (induced by source paper "advice complexity of treasure hunt in geometric terrains"):
  with members:
    - "reaching a target in the plane with no information"
    - "impact of knowledge on election time in anonymous networks"
    - "topology recognition with advice"
    - "deterministic graph exploration with advice"
    - "tradeoffs between cost and information for rendezvous and treasure hunt"

Connected through hyperedge HE-2 (induced by source paper "cost vs information tradeoffs for treasure hunt in the plane"):
  with members:
    - "information complexity of treasure hunt in geometric terrains"
    - "reaching a target in the plane with no information"
    - "impact of knowledge on election time in anonymous networks"
    - "topology recognition with advice"
    - "deterministic graph exploration with advice"
    - "time vs information tradeoffs for leader election in anonymous trees"
    - "tradeoffs between cost and information for rendezvous and treasure hunt"

Connected through hyperedge HE-3 (induced by source paper "information complexity of treasure hunt in geometric terrains"):
  with members:
    - "reaching a target in the plane with no information"
    - "impact of knowledge on election time in anonymous networks"
    - "topology recognition with advice"
    - "deterministic graph exploration with advice"
    - "time vs information tradeoffs for leader election in anonymous trees"

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.CC(Computational Complexity)