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 "cost vs information tradeoffs for treasure hunt in the plane"):
  Abstract: A mobile agent has to find an inert treasure hidden in the plane. Both the agent and the treasure are modeled as points. This is a variant of the task known as treasure hunt. The treasure is at a distance at most $D$ from the initial position of the agent, and the agent finds the treasure when it gets at distance $r$ from it, called the {\em vision radius}. However, the agent does not know the location of the treasure and does not know the parameters $D$ and $r$. The cost of finding the treasure is the length of the trajectory of the agent. We investigate the tradeoffs between the amount of information held {\em a priori} by the agent and the cost of treasure hunt. Following the well-established paradigm of {\em algorithms with advice}, this information is given to the agent in advance as a binary string, by an oracle cooperating with the agent and knowing the location of the treasure and the initial position of the agent. The size of advice given to the agent is the length of this binary string. #R##N#For any size $z$ of advice and any $D$ and $r$, let $OPT(z,D,r)$ be the optimal cost of finding the treasure for parameters $z$, $D$ and $r$, if the agent has only an advice string of length $z$ as input. We design treasure hunt algorithms working with advice of size $z$ at cost $O(OPT(z,D,r))$ whenever $r\leq 1$ or $r\geq 0.9D$. For intermediate values of $r$, i.e., $1 0$, the treasure can be found at cost $O(OPT(z,D,r)^{1+\alpha})$.

Includes members:
  Member "information complexity of treasure hunt in geometric terrains":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lower bounds for shoreline searching with 2 or more robots")
  Member "the graph exploration problem with advice":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "advice complexity of treasure hunt in geometric terrains")
  Member "reaching a target in the plane with no information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "advice complexity of treasure hunt in geometric terrains")
  Member "impact of knowledge on election time in anonymous networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "advice complexity of treasure hunt in geometric terrains")
  Member "topology recognition with advice":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "advice complexity of treasure hunt in geometric terrains")
  Member "deterministic graph exploration with advice":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "advice complexity of treasure hunt in geometric terrains")
  Member "time vs information tradeoffs for leader election in anonymous trees":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deterministic leader election in anonymous radio networks")
  Member "tradeoffs between cost and information for rendezvous and treasure hunt":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "advice complexity of treasure hunt in geometric terrains")

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)