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: "active rendezvous for multi robot pose graph optimization using sensing over wi fi"
  Abstract: We present a novel framework for collaboration amongst a team of robots performing Pose Graph Optimization (PGO) that addresses two important challenges for multi-robot SLAM: that of enabling information exchange "on-demand" via active rendezvous, and that of rejecting outlier measurements with high probability. Our key insight is to exploit relative position data present in the communication channel between agents, as an independent measurement in PGO. We show that our algorithmic and experimental framework for integrating Channel State Information (CSI) over the communication channels, with multi-agent PGO, addresses the two open challenges of enabling information exchange and rejecting outliers. Our presented framework is distributed and applicable in low-lighting or featureless environments where traditional sensors often fail. We present extensive experimental results on actual robots showing both the use of active rendezvous resulting in error reduction by 6X as compared to randomly occurring rendezvous and the use of CSI observations providing a reduction in ground truth pose estimation errors of 32%. These results demonstrate the promise of using a combination of multi-robot coordination and CSI to address challenges in multi-agent localization and mapping -- providing an important step towards integrating communication as a novel sensor for SLAM tasks.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning for pomdp partitioned rollout and policy iteration with application to autonomous sequential repair problems"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "decentralized control of partially observable markov decision processes using belief space macro actions"
    - "solving pomdps by searching in policy space"
    - "solving pomdps by searching the space of finite policies"
    - "a function approximation approach to estimation of policy gradient for pomdp with structured policies"
    - "discretized approximations for pomdp with average cost"

Connected through hyperedge HE-2 (induced by source paper "door slam distributed online and outlier resilient slam for robotic teams"):
  with members:
    - "a resource aware approach to collaborative loop closure detection with provable performance guarantees"
    - "convex relaxations for pose graph optimization with outliers"
    - "near optimal budgeted data exchange for distributed loop closure detection"
    - "modeling perceptual aliasing in slam via discrete continuous graphical models"
    - "talk resource efficiently to me optimal communication planning for distributed loop closure detection"
    - "data efficient decentralized visual slam"
    - "distributed mapping with privacy and communication constraints lightweight algorithms and object based models"
    - "netvlad cnn architecture for weakly supervised place recognition"

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)