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 "a framework for searching in graphs in the presence of errors"):
  Abstract: We consider two types of searching models, where the goal is to design an adaptive algorithm that locates an unknown vertex in a graph by repeatedly performing queries. In the vertex-query model, each query points to a vertex $v$ and the response either admits that $v$ is the target or provides a neighbor of $v$ on a shortest path from $v$ to the target. This model has been introduced for trees by Onak and Parys [FOCS 2006] and by Emamjomeh-Zadeh et al. [STOC 2016] for arbitrary graphs. In the edge-query model, each query chooses an edge and the response reveals which endpoint of the edge is closer to the target, breaking ties arbitrarily. #R##N#Our goal is to analyze solutions to these problems assuming that some responses may be erroneous. We develop a scheme for tackling such noisy models with the following line of arguments: For each of the two models, we analyze a generic strategy that assumes a fixed number of lies and give a precise bound for its length via an amortized analysis. From this, we derive bounds for both a linearly bounded error rate, where the number of errors in $T$ queries is bounded by $rT$ for some $r<1/2$, and a probabilistic model in which each response is incorrect with some probability $p<1/2$. The bounds for adversarial case turn out to be strong enough for non-adversarial scenarios as well. #R##N#We obtain thus a much simpler strategy performing fewer vertex-queries than one by Emamjomeh-Zadeh et al. [STOC 2016]. For edge-queries, not studied before for general graphs, we obtain bounds that are tight up to $\log \Delta$ factors in all error models. Applying our graph-theoretic results to the setting of edge-queries for paths, we obtain a number of improvements over existing bounds for searching in a sorted array in the presence of errors, including an exponential improvement for the prefix-bounded model in unbounded domains.

Includes members:
  Member "binary search in graphs revisited":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "searching trees with permanently noisy advice walking and query algorithms")
  Member "approximation strategies for generalized binary search in weighted trees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "binary search in graphs revisited")
  Member "a general framework for robust interactive learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "diameter based interactive structure discovery")
  Member "on the tree search problem with non uniform costs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "competitive online search trees on trees")
  Member "deterministic and probabilistic binary search in graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "corrupted multidimensional binary search learning in the presence of irrational agents")
  Member "local algorithms for interactive clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "diameter based interactive structure discovery")
  Member "query chains learning to rank from implicit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "semeval 2016 task 3 community question answering")

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)