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: "collaborative search on the plane without communication"
  Abstract: We use distributed computing tools to provide a new perspective on the behavior of cooperative biological ensembles. We introduce the Ants Nearby Treasure Search (ANTS) problem, a generalization of the classical cow-path problem [10, 20, 41, 42], which is relevant for collective foraging in animal groups. In the ANTS problem, k identical (probabilistic) agents, initially placed at some central location, collectively search for a treasure in the two-dimensional plane. The treasure is placed at a target location by an adversary and the goal is to find it as fast as possible as a function of both k and D, where D is the distance between the central location and the target. This is biologically motivated by cooperative, central place foraging, such as performed by ants around their nest. In this type of search there is a strong preference to locate nearby food sources before those that are further away. We focus on trying to find what can be achieved if communication is limited or altogether absent. Indeed, to avoid overlaps agents must be highly dispersed making communication difficult. Furthermore, if the agents do not commence the search in synchrony, then even initial communication is problematic. This holds, in particular, with respect to the question of whether the agents can communicate and conclude their total number, k. It turns out that the knowledge of k by the individual agents is crucial for performance. Indeed, it is a straightforward observation that the time required for finding the treasure is Ω(D + D2/k), and we show in this paper that this bound can be matched if the agents have knowledge of k up to some constant approximation.   We present a tight bound for the competitive penalty that must be paid, in the running time, if the agents have no information about k. Specifically, this bound is slightly more than logarithmic in the number of agents. In addition, we give a lower bound for the setting in which the agents are given some estimation of k. Informally, our results imply that the agents can potentially perform well without any knowledge of their total number k, however, to further improve, they must use some information regarding k. Finally, we propose a uniform algorithm that is both efficient and extremely simple, suggesting its relevance for actual biological scenarios.

Connected through hyperedge HE-1 (induced by source paper "probabilistically faulty searching on a half line"):
  with members:
    - "energy consumption of group search on a line"
    - "average case worst case tradeoffs for evacuating 2 robots from the disk in the face to face model"
    - "linear search with terrain dependent speeds"
    - "fast two robot disk evacuation with wireless communication"
    - "search and fetch with 2 robots on a disk wireless and face to face communication models"
    - "further connections between contract scheduling and ray searching problems"
    - "online searching with turn cost"

Connected through hyperedge HE-2 (induced by source paper "a coordinated search strategy for multiple solitary robots an extension"):
  with members:
    - "a coordinated search strategy for solitary robots"

Connected through hyperedge HE-3 (induced by source paper "evacuating equilateral triangles and squares in the face to face model"):
  with members:
    - "god save the queen"
    - "priority evacuation from a disk using mobile robots"
    - "evacuating robots from a disk using face to face communication"
    - "trade offs between selection complexity and performance when searching the plane without communication"

Connected through hyperedge HE-4 (induced by source paper "the ants problem"):
  with members:
    - "parallel linear search with no coordination for a randomly placed treasure"
    - "parallel exhaustive search without coordination"
    - "trade offs between selection complexity and performance when searching the plane without communication"
    - "toward more localized local algorithms removing assumptions concerning global knowledge"
    - "memory lower bounds for randomized collaborative search and applications to biology"
    - "beeping a maximal independent set"
    - "physarum can compute shortest paths"
    - "online searching with turn cost"

Connected through hyperedge HE-5 (induced by source paper "lower bounds for searching robots some faulty"):
  with members:
    - "search on a line by byzantine robots"
    - "revisiting the problem of searching on a line"
    - "online searching with turn cost"
    - "optimal constructions of hybrid algorithms"

Connected through hyperedge HE-6 (induced by source paper "parallel search with no coordination"):
  with members:
    - "linear search with terrain dependent speeds"
    - "parallel exhaustive search without coordination"
    - "memory lower bounds for randomized collaborative search and applications to biology"

Connected through hyperedge HE-7 (induced by source paper "parallel linear search with no coordination for a randomly placed treasure"):
  with members:
    - "parallel exhaustive search without coordination"
    - "trade offs between selection complexity and performance when searching the plane without communication"
    - "memory lower bounds for randomized collaborative search and applications to biology"
    - "online searching with turn cost"

Connected through hyperedge HE-8 (induced by source paper "parallel exhaustive search without coordination"):
  with members:
    - "trade offs between selection complexity and performance when searching the plane without communication"
    - "memory lower bounds for randomized collaborative search and applications to biology"
    - "online searching with turn cost"

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.DC(Distributed, Parallel, and Cluster Computing)