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: "scalable asymptotically optimal multi robot motion planning"
  Abstract: Finding asymptotically-optimal paths in multi-robot motion planning problems could be achieved, in principle, using sampling-based planners in the composite configuration space of all of the robots in the space. The dimensionality of this space increases with the number of robots, rendering this approach impractical. This work focuses on a scalable sampling-based planner for coupled multi-robot problems that provides asymptotic optimality. It extends the dRRT approach, which proposed building roadmaps for each robot and searching an implicit roadmap in the composite configuration space. This work presents a new method, dRRT* , and develops theory for scalable convergence to optimal paths in multi-robot problems. Simulated experiments indicate dRRT* converges to high-quality paths while scaling to higher numbers of robots where the naive approach fails. Furthermore, dRRT* is applicable to high-dimensional problems, such as planning for robot manipulators

Connected through hyperedge HE-1 (induced by source paper "drrt scalable and informed asymptotically optimal multi robot motion planning"):
  with members:
    - "effective metrics for multi robot motion planning"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "motion planning for unlabeled discs with optimality guarantees"
    - "on the hardness of unlabeled multi robot motion planning"
    - "optimal sampling based motion planning under differential constraints the drift case with linear affine dynamics"
    - "efficient multi robot motion planning for unlabeled discs in simple polygons"
    - "finding a needle in an exponential haystack discrete rrt for exploration of implicit roadmaps in multi robot motion planning"
    - "k color multi robot motion planning"

Connected through hyperedge HE-2 (induced by source paper "anytime multi arm task and motion planning for pick and place of individual objects via handoffs"):
  with members:
    - "drrt scalable and informed asymptotically optimal multi robot motion planning"
    - "towards robust product packing with a minimalistic end effector"
    - "admissible abstractions for near optimal task and motion planning"
    - "fast high quality dual arm rearrangement in synchronous monotone tabletop setups"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "analysis and observations from the first amazon picking challenge"
    - "finding a needle in an exponential haystack discrete rrt for exploration of implicit roadmaps in multi robot motion planning"
    - "on the power of manifold samples in exploring configuration spaces and the dimensionality of narrow passages"

Connected through hyperedge HE-3 (induced by source paper "multi agent pathfinding with continuous time"):
  with members:
    - "lifelong path planning with kinematic constraints for multi agent pickup and delivery"
    - "feasibility study moving non homogeneous teams in congested video game environments"
    - "alan adaptive learning for multi agent navigation"
    - "multi agent path finding with delay probabilities"

Connected through hyperedge HE-4 (induced by source paper "a modular framework for motion planning using safe by design motion primitives"):
  with members:
    - "a framework for multi vehicle navigation using feedback based motion primitives"
    - "downwash aware trajectory planning for large quadrotor teams"
    - "funnel libraries for real time robust feedback motion planning"
    - "an effective algorithmic framework for near optimal multi robot path planning"

Connected through hyperedge HE-5 (induced by source paper "fast high quality dual arm rearrangement in synchronous monotone tabletop setups"):
  with members:
    - "coordinating the motion of labeled discs with optimality guarantees under extreme density"
    - "complexity results and fast methods for optimal tabletop rearrangement with overhand grasps"
    - "characterizing minimum length coordinated motions for two discs"
    - "motion planning for unlabeled discs with optimality guarantees"
    - "on the hardness of unlabeled multi robot motion planning"
    - "efficient multi robot motion planning for unlabeled discs in simple polygons"
    - "asymptotically optimal algorithms for one to one pickup and delivery problems with applications to transportation systems"
    - "finding a needle in an exponential haystack discrete rrt for exploration of implicit roadmaps in multi robot motion planning"

Connected through hyperedge HE-6 (induced by source paper "the critical radius in sampling based motion planning"):
  with members:
    - "drrt scalable and informed asymptotically optimal multi robot motion planning"
    - "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"
    - "safe motion planning in unknown environments optimality benchmarks and tractable policies"
    - "minimizing task space frechet error via efficient incremental graph search"
    - "new perspective on sampling based motion planning via random geometric graphs"
    - "collision detection or nearest neighbor search on the computational bottleneck in sampling based motion planning"
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"

Connected through hyperedge HE-7 (induced by source paper "effective metrics for multi robot motion planning"):
  with members:
    - "on the hardness of unlabeled multi robot motion planning"
    - "efficient multi robot motion planning for unlabeled discs in simple polygons"
    - "finding a needle in an exponential haystack discrete rrt for exploration of implicit roadmaps in multi robot motion planning"
    - "k color multi robot motion planning"
    - "on the power of manifold samples in exploring configuration spaces and the dimensionality of narrow passages"
    - "sampling based algorithms for optimal motion planning"
    - "pspace completeness of sliding block puzzles and other problems through the nondeterministic constraint logic model of computation"

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.MA(Multiagent Systems)