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: "memory lower bounds for randomized collaborative search and applications to biology"
  Abstract: Initial knowledge regarding group size can be crucial for collective performance. We study this relation in the context of the {\em Ants Nearby Treasure Search (ANTS)} problem \cite{FKLS}, which models natural cooperative foraging behavior such as that performed by ants around their nest. In this problem, $k$ (probabilistic) agents, initially placed at some 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 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. It is easy to see that $T=\Omega(D+D^2/k)$ time units are necessary for finding the treasure. Recently, it has been established that $O(T)$ time is sufficient if the agents know their total number $k$ (or a constant approximation of it), and enough memory bits are available at their disposal \cite{FKLS}. In this paper, we establish lower bounds on the agent memory size required for achieving certain running time performances. To the best our knowledge, these bounds are the first non-trivial lower bounds for the memory size of probabilistic searchers. For example, for every given positive constant $\epsilon$, terminating the search by time $O(\log^{1-\epsilon} k \cdot T)$ requires agents to use $\Omega(\log\log k)$ memory bits. Such distributed computing bounds may provide a novel, strong tool for the investigation of complex biological systems.

Connected through hyperedge HE-1 (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"
    - "collaborative search on the plane without communication"
    - "beeping a maximal independent set"
    - "physarum can compute shortest paths"
    - "online searching with turn cost"

Connected through hyperedge HE-2 (induced by source paper "parallel search with no coordination"):
  with members:
    - "linear search with terrain dependent speeds"
    - "parallel exhaustive search without coordination"
    - "collaborative search on the plane without communication"

Connected through hyperedge HE-3 (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"
    - "collaborative search on the plane without communication"
    - "online searching with turn cost"

Connected through hyperedge HE-4 (induced by source paper "parallel exhaustive search without coordination"):
  with members:
    - "trade offs between selection complexity and performance when searching the plane without communication"
    - "collaborative search on the plane without communication"
    - "online searching with turn cost"

Connected through hyperedge HE-5 (induced by source paper "distributed house hunting in ant colonies"):
  with members:
    - "breathe before speaking efficient information dissemination despite noisy limited and anonymous communication"
    - "trade offs between selection complexity and performance when searching the plane without communication"
    - "collaborative search on the plane without communication"

Connected through hyperedge HE-6 (induced by source paper "idle ants have a role"):
  with members:
    - "collaborative search on the plane without communication"

Connected through hyperedge HE-7 (induced by source paper "optimal and resilient pheromone utilization in ant foraging"):
  with members:
    - "collaborative search on the plane without communication"

Connected through hyperedge HE-8 (induced by source paper "breathe before speaking efficient information dissemination despite noisy limited and anonymous communication"):
  with members:
    - "the power of two choices in distributed voting"
    - "optimal gossip with direct addressing"
    - "simple dynamics for plurality consensus"
    - "collaborative search on the plane without communication"
    - "beeping a maximal independent set"
    - "simple fast and deterministic gossip and rumor spreading"
    - "global computation in a poorly connected world fast rumor spreading with no dependence on conductance"
    - "natural models for evolution on networks"

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)