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: "planning for robotic exploration based on forward simulation"
  Abstract: Abstract   We address the problem of controlling a mobile robot to explore a partially known environment. The robot’s objective is the maximization of the amount of information collected about the environment. We formulate the problem as a partially observable Markov decision process (POMDP) with an information-theoretic objective function, and solve it applying forward simulation algorithms with an open-loop approximation. We present a new sample-based approximation for mutual information useful in mobile robotics. The approximation can be seamlessly integrated with forward simulation planning algorithms. We investigate the usefulness of POMDP based planning for exploration, and to alleviate some of its weaknesses propose a combination with frontier based exploration. Experimental results in simulated and real environments show that, depending on the environment, applying POMDP based planning for exploration can improve performance over frontier exploration.

Connected through hyperedge HE-1 (induced by source paper "functional path optimisation for exploration in continuous occupancy maps"):
  with members:
    - "stochastic functional gradient for motion planning in continuous occupancy maps"
    - "occupancy map building through bayesian exploration"
    - "functional gradient motion planning in reproducing kernel hilbert spaces"

Connected through hyperedge HE-2 (induced by source paper "hypermap mapping framework and its application to autonomous semantic exploration"):
  with members:
    - "yolact real time instance segmentation"
    - "deep network uncertainty maps for indoor navigation"
    - "the effect of color space selection on detectability and discriminability of colored objects"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-3 (induced by source paper "look before you sweep visibility aware motion planning"):
  with members:
    - "safe motion planning in unknown environments optimality benchmarks and tractable policies"
    - "provably safe robot navigation with obstacle uncertainty"

Connected through hyperedge HE-4 (induced by source paper "proactive intention recognition for joint human robot search and rescue missions through monte carlo planning in pomdp environments"):
  with members:
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "unifying count based exploration and intrinsic motivation"

Connected through hyperedge HE-5 (induced by source paper "multi robot active information gathering with periodic communication"):
  with members:
    - "decentralized control of partially observable markov decision processes using belief space macro actions"
    - "maa a heuristic search algorithm for solving decentralized pomdps"
    - "optimal and approximate q value functions for decentralized pomdps"
    - "the communicative multiagent team decision problem analyzing teamwork theories and models"

Connected through hyperedge HE-6 (induced by source paper "occupancy map building through bayesian exploration"):
  with members:
    - "functional gradient motion planning in reproducing kernel hilbert spaces"
    - "feedback motion planning under non gaussian uncertainty and non convex state constraints"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

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)