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: "constant factor time optimal multi robot routing on high dimensional grids in mostly sub quadratic time"
  Abstract: Let $G = (V, E)$ be an $m_1 \times \ldots \times m_k$ grid. Assuming that each $v \in V$ is occupied by a robot and a robot may move to a neighboring vertex in a step via synchronized rotations along cycles of $G$, we first establish that the arbitrary reconfiguration of labeled robots on $G$ can be performed in $O(k\sum_i m_i)$ makespan and requires $O(|V|^2)$ running time in the worst case and $o(|V|^2)$ when $G$ is non-degenerate (i.e., nearly one dimensional). The resulting algorithm, \isag, provides average case $O(1)$-approximate (i.e., constant factor) time optimality guarantee. In the case when all dimensions are of similar size $O(|V|^{\frac{1}{k}})$, the running time of \isag approaches a linear $O(|V|)$. Define $d_g(p)$ as the largest distance between individual initial and goal configurations over all robots for a given problem instance $p$, building on \isag, we develop the \pafalgo (\paf) algorithm that computes $O(d_g(p))$ makespan solutions for $k = 2, 3$ using mostly $o(|V|^2)$ running time. \paf provides worst case $O(1)$-approximation regarding solution time optimality. We note that the worst case running time for the problem is $\Omega(|V|^2)$.

Connected through hyperedge HE-1 (induced by source paper "ddm fast near optimal multi robot path planning using diversified path and optimal sub problem solution database heuristics"):
  with members:
    - "multi agent pathfinding definitions variants and benchmarks"
    - "priority inheritance with backtracking for iterative multi agent path finding"
    - "automatic algorithm selection in multi agent pathfinding"
    - "coordinating the motion of labeled discs with optimality guarantees under extreme density"
    - "x anytime multiagent planning with bounded search"
    - "lifelong multi agent path finding for online pickup and delivery tasks"
    - "learning the problem optimum map analysis and application to global optimization in robotics"
    - "motion planning for unlabeled discs with optimality guarantees"

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.RO(Robotics)