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: "searching on trees with noisy memory"
  Abstract: We consider a search problem on trees using unreliable guiding instructions. Specifically, an agent starts a search at the root of a tree aiming to find a treasure hidden at one of the nodes by an adversary. Each visited node holds information, called advice, regarding the most promising neighbor to continue the search. However, the memory holding this information may be unreliable. Modeling this scenario, we focus on a probabilistic setting. That is, the advice at a node is a pointer to one of its neighbors. With probability $q$ each node is faulty, independently of other nodes, in which case its advice points at an arbitrary neighbor, chosen u.a.r. Otherwise, the node is sound and necessarily points at the correct neighbor. Crucially, the advice is permanent, in the sense that querying a node several times would yield the same answer. We evaluate the agent's efficiency by two measures: The move complexity denotes the expected number of edge traversals, and the query complexity denotes the expected number of queries. #R##N#Let $\Delta$ denote the maximal degree. Roughly speaking, the main message of this paper is that in order to obtain efficient search, $1/\sqrt{\Delta}$ is a threshold for the noise parameter $q$. Essentially, we prove that above the threshold, every search algorithm has query complexity (and move complexity) which is both exponential in the depth $d$ of the treasure and polynomial in the number of nodes $n$. Conversely, below the threshold, there exists an algorithm with move complexity $O(d\sqrt{\Delta})$, and an algorithm with query complexity $O(\sqrt{\Delta}\log \Delta \log^2 n)$. Moreover, for the case of regular trees, we obtain an algorithm with query complexity $O(\sqrt{\Delta}\log n\log\log n)$. The move complexity bound is tight below the threshold and the query complexity bounds are not far from the lower bound of $\Omega(\sqrt{\Delta}\log_\Delta n)$.

Connected through hyperedge HE-1 (induced by source paper "distributed community detection via metastability of the 2 choices dynamics"):
  with members:
    - "pooling or sampling collective dynamics for electrical flow estimation"
    - "phase transition of the 2 choices dynamics on core periphery networks"
    - "the ants problem"
    - "limits for rumor spreading in stochastic populations"
    - "ignore or comply on breaking symmetry in consensus"
    - "distributed graph clustering and sparsification"
    - "average whenever you meet opportunistic protocols for community detection"
    - "a tight analysis of the parallel undecided state dynamics with two colors"

Connected through hyperedge HE-2 (induced by source paper "binary search in graphs revisited"):
  with members:
    - "adaptive hierarchical clustering using ordinal queries"
    - "approximation strategies for generalized binary search in weighted trees"
    - "a general framework for robust interactive learning"
    - "deterministic and probabilistic binary search in graphs"

Connected through hyperedge HE-3 (induced by source paper "the dependent doors problem an investigation into sequential decisions without feedback"):
  with members:
    - "deterministic and probabilistic binary search in graphs"
    - "toward more localized local algorithms removing assumptions concerning global knowledge"
    - "on the complexity of trial and error"

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)