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: "deterministic graph exploration with advice"
  Abstract: We consider the task of graph exploration. An $n$-node graph has unlabeled nodes, and all ports at any node of degree $d$ are arbitrarily numbered $0,\dots, d-1$. A mobile agent has to visit all nodes and stop. The exploration time is the number of edge traversals. We consider the problem of how much knowledge the agent has to have a priori, in order to explore the graph in a given time, using a deterministic algorithm. This a priori information (advice) is provided to the agent by an oracle, in the form of a binary string, whose length is called the size of advice. We consider two types of oracles. The instance oracle knows the entire instance of the exploration problem, i.e., the port-numbered map of the graph and the starting node of the agent in this map. The map oracle knows the port-numbered map of the graph but does not know the starting node of the agent. #R##N#We first consider exploration in polynomial time, and determine the exact minimum size of advice to achieve it. This size is $\log\log\log n -\Theta(1)$, for both types of oracles. #R##N#When advice is large, there are two natural time thresholds: $\Theta(n^2)$ for a map oracle, and $\Theta(n)$ for an instance oracle, that can be achieved with sufficiently large advice. We show that, with a map oracle, time $\Theta(n^2)$ cannot be improved in general, regardless of the size of advice. We also show that the smallest size of advice to achieve this time is larger than $n^\delta$, for any $\delta <1/3$. #R##N#For an instance oracle, advice of size $O(n\log n)$ is enough to achieve time $O(n)$. We show that, with any advice of size $o(n\log n)$, the time of exploration must be at least $n^\epsilon$, for any $\epsilon <2$, and with any advice of size $O(n)$, the time must be $\Omega(n^2)$. #R##N#We also investigate minimum advice sufficient for fast exploration of hamiltonian graphs.

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

Connected through hyperedge HE-2 (induced by source paper "improved lower bound for competitive graph exploration"):
  with members:
    - "online graph exploration on a restricted graph class optimal solutions for tadpole graphs"
    - "a general lower bound for collaborative tree exploration"

Connected through hyperedge HE-3 (induced by source paper "cost vs information tradeoffs for treasure hunt in the plane"):
  with members:
    - "information complexity of treasure hunt in geometric terrains"
    - "the graph exploration problem with advice"
    - "reaching a target in the plane with no information"
    - "impact of knowledge on election time in anonymous networks"
    - "topology recognition 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-4 (induced by source paper "advice complexity of adaptive priority algorithms"):
  with members:
    - "advice complexity of priority algorithms"
    - "greedy matching guarantees and limitations"
    - "advice complexity of the online induced subgraph problem"
    - "an o 1 0821 n time algorithm for computing maximum independent set in graphs with bounded degree 3"

Connected through hyperedge HE-5 (induced by source paper "information complexity of treasure hunt in geometric terrains"):
  with members:
    - "the graph exploration problem with advice"
    - "reaching a target in the plane with no information"
    - "impact of knowledge on election time in anonymous networks"
    - "topology recognition with advice"
    - "time vs information tradeoffs for leader election in anonymous trees"

Connected through hyperedge HE-6 (induced by source paper "efficient dispersion of mobile robots on graphs"):
  with members:
    - "dispersion of mobile robots a study of memory time trade offs"
    - "a general lower bound for collaborative tree exploration"
    - "time and space optimality of rotor router graph exploration"
    - "algorithms for rapidly dispersing robot swarms in unknown environments"

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)