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: "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.

Connected through hyperedge HE-1 (induced by source paper "multi round cooperative search games with multiple players"):
  with members:
    - "the impact of local information on the performance of multiagent systems"
    - "bayesian incentive compatible bandit exploration"
    - "online makespan minimization with parallel schedules"
    - "computer science and game theory a brief survey"
    - "a computer scientist looks at game theory"

Connected through hyperedge HE-2 (induced by source paper "evacuating two robots from a disk a second cut"):
  with members:
    - "god save the queen"
    - "priority evacuation from a disk using mobile robots"
    - "fast two robot disk evacuation with wireless communication"
    - "a quasi polynomial time partition oracle for graphs with an excluded minor"

Connected through hyperedge HE-3 (induced by source paper "a most irrational foraging algorithm"):
  with members:
    - "quasi regular sequences and optimal schedules for security games"

Connected through hyperedge HE-4 (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"
    - "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"
    - "on the parallel undecided state dynamics with two colors"

Connected through hyperedge HE-5 (induced by source paper "cooperative search games symmetric equilibria robustness and price of anarchy"):
  with members:
    - "the impact of local information on the performance of multiagent systems"
    - "bayesian incentive compatible bandit exploration"
    - "online makespan minimization with parallel schedules"
    - "computer science and game theory a brief survey"
    - "a computer scientist looks at game theory"

Connected through hyperedge HE-6 (induced by source paper "intense competition can drive selfish explorers to optimize coverage"):
  with members:
    - "bayesian incentive compatible bandit exploration"
    - "parallel exhaustive search without coordination"
    - "online makespan minimization with parallel schedules"

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)