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: "policy contingent abstraction for robust robot control"
  Abstract: This paper presents a scalable control algorithm that enables a deployed mobile robot system to make high-level decisions under full consideration of its probabilistic belief. Our approach is based on insights from the rich literature of hierarchical controllers and hierarchical MDPs. The resulting controller has been successfully deployed in a nursing facility near Pittsburgh, PA. To the best of our knowledge, this work is a unique instance of applying POMDPs to high-level robotic control problems.

Connected through hyperedge HE-1 (induced by source paper "learning hybrid object kinematics for efficient hierarchical planning under uncertainty"):
  with members:
    - "category level articulated object pose estimation"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "unsupervised discovery of parts structure and dynamics"
    - "inferring geometric constraints in human demonstrations"
    - "efficient hierarchical robot motion planning under uncertainty and hybrid dynamics"
    - "prediction and control with temporal segment models"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "se3 nets learning rigid body motion using deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "pomdps make better hackers accounting for uncertainty in penetration testing"):
  with members:
    - "penetration testing pomdp solving"
    - "attack planning in the real world"
    - "an algorithm to find optimal attack paths in nondeterministic scenarios"
    - "the metric ff planning system translating ignoring delete lists to numeric state variables"

Connected through hyperedge HE-3 (induced by source paper "les pomdp font de meilleurs hackers tenir compte de l incertitude dans les tests de penetration"):
  with members:
    - "penetration testing pomdp solving"
    - "attack planning in the real world"
    - "an algorithm to find optimal attack paths in nondeterministic scenarios"
    - "the metric ff planning system translating ignoring delete lists to numeric state variables"

Connected through hyperedge HE-4 (induced by source paper "multiscale markov decision problems compression solution and transfer learning"):
  with members:
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
    - "finding sparse cuts locally using evolving sets"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-5 (induced by source paper "hierarchical pomdp controller optimization by likelihood maximization"):
  with members:
    - "learning finite state controllers for partially observable environments"
    - "heuristic search value iteration for pomdps"

Connected through hyperedge HE-6 (induced by source paper "efficient planning under uncertainty with macro actions"):
  with members:
    - "approximate planning for factored pomdps using belief state simplification"
    - "point based pomdp algorithms improved analysis and implementation"
    - "heuristic search value iteration for pomdps"
    - "a scalable method for solving high dimensional continuous pomdps using local approximation"
    - "online planning algorithms for pomdps"

Connected through hyperedge HE-7 (induced by source paper "finding approximate pomdp solutions through belief compression"):
  with members:
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "solving pomdps by searching in policy space"
    - "tractable inference for complex stochastic processes"
    - "pegasus a policy search method for large mdps and pomdps"
    - "learning finite state controllers for partially observable environments"
    - "solving pomdps by searching the space of finite policies"
    - "learning geometrically constrained hidden markov models for robot navigation bridging the topological geometrical gap"
    - "speeding up the convergence of value iteration in partially observable markov decision processes"

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)