human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "door slam distributed online and outlier resilient slam for robotic teams"):
  Abstract: To achieve collaborative tasks, robots in a team need to have a shared understanding of the environment and their location within it. Distributed Simultaneous Localization and Mapping (SLAM) offers a practical solution to localize the robots without relying on an external positioning system (e.g. GPS) and with minimal information exchange. Unfortunately, current distributed SLAM systems are vulnerable to perception outliers and therefore tend to use very conservative parameters for inter-robot place recognition. However, being too conservative comes at the cost of rejecting many valid loop closure candidates, which results in less accurate trajectory estimates. This paper introduces DOOR-SLAM, a fully distributed SLAM system with an outlier rejection mechanism that can work with less conservative parameters. DOOR-SLAM is based on peer-to-peer communication and does not require full connectivity among the robots. DOOR-SLAM includes two key modules: a pose graph optimizer combined with a distributed pairwise consistent measurement set maximization algorithm to reject spurious inter-robot loop closures; and a distributed SLAM front-end that detects inter-robot loop closures without exchanging raw sensor data. The system has been evaluated in simulations, benchmarking datasets, and field experiments, including tests in GPS-denied subterranean environments. DOOR-SLAM produces more inter-robot loop closures, successfully rejects outliers, and results in accurate trajectory estimates, while requiring low communication bandwidth. Full source code is available at this https URL.

Includes members:
  Member "active rendezvous for multi robot pose graph optimization using sensing over wi fi":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reinforcement learning for pomdp partitioned rollout and policy iteration with application to autonomous sequential repair problems")
  Member "a resource aware approach to collaborative loop closure detection with provable performance guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "measurement scheduling for cooperative localization in resource constrained conditions")
  Member "convex relaxations for pose graph optimization with outliers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convex optimisation for inverse kinematics")
  Member "near optimal budgeted data exchange for distributed loop closure detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "resource aware algorithms for distributed loop closure detection with provable performance guarantees")
  Member "modeling perceptual aliasing in slam via discrete continuous graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "teaser fast and certifiable point cloud registration")
  Member "talk resource efficiently to me optimal communication planning for distributed loop closure detection":
    also connected through other hyperedges:
      (none)
  Member "data efficient decentralized visual slam":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "kidnapped radar topological radar localisation using rotationally invariant metric learning")
  Member "distributed mapping with privacy and communication constraints lightweight algorithms and object based models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "search and rescue under the forest canopy using multiple uavs")

Question: Please tell me which class the source paper 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)