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 "the ants problem"):
  Abstract: We introduce the Ants Nearby Treasure Search problem, which models natural cooperative foraging behavior such as that performed by ants around their nest. In this problem, k probabilistic agents, initially placed at a central location, collectively search for a treasure on the two-dimensional grid. The treasure is placed at a target location by an adversary and the agents' goal is to find it as fast as possible as a function of both k and D, where D is the (unknown) distance between the central location and the target. We concentrate on the case in which agents cannot communicate while searching. It is straightforward to see that the time until at least one agent finds the target is at least $${\varOmega }(D+D^2/k)$$Ω(D+D2/k), even for very sophisticated agents, with unrestricted memory. Our algorithmic analysis aims at establishing connections between the time complexity and the initial knowledge held by agents (e.g., regarding their total number k), as they commence the search. We provide a range of both upper and lower bounds for the initial knowledge required for obtaining fast running time. For example, we prove that $$\log \log k+{\varTheta }(1)$$loglogk+ź(1) bits of initial information are both necessary and sufficient to obtain asymptotically optimal running time, i.e., $${\mathcal {O}}(D+D^2/k)$$O(D+D2/k). We also we prove that for every $$0<\epsilon < 1$$0<∈<1, running in time $${\mathcal {O}}(\log ^{1-\epsilon } k\cdot (D+D^2/k))$$O(log1-∈k·(D+D2/k)) requires that agents have the capacity for storing $${\varOmega }(\log ^\epsilon k)$$Ω(log∈k) different states as they leave the nest to start the search. To the best of our knowledge, the lower bounds presented in this paper provide the first non-trivial lower bounds on the memory complexity of probabilistic agents in the context of search problems. We view this paper as a "proof of concept" for a new type of interdisciplinary methodology. To fully demonstrate this methodology, the theoretical tradeoff presented here (or a similar one) should be combined with measurements of the time performance of searching ants.

Includes members:
  Member "parallel linear search with no coordination for a randomly placed treasure":
    also connected through other hyperedges:
      (none)
  Member "parallel exhaustive search without coordination":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "intense competition can drive selfish explorers to optimize coverage")
  Member "trade offs between selection complexity and performance when searching the plane without communication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lower bounds for shoreline searching with 2 or more robots")
  Member "toward more localized local algorithms removing assumptions concerning global knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faster deterministic distributed coloring through recursive list coloring")
  Member "memory lower bounds for randomized collaborative search and applications to biology":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parallel search with no coordination")
  Member "collaborative search on the plane without communication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistically faulty searching on a half line")
  Member "beeping a maximal independent set":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed voting in beep model")
  Member "physarum can compute shortest paths":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improved convergence for ell_ infty and ell_1 regression via iteratively reweighted least squares")

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.DC(Distributed, Parallel, and Cluster Computing)