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: "finding approximate pomdp solutions through belief compression"
  Abstract: Standard value function approaches to finding policies for Partially Observable Markov Decision Processes (POMDPs) are generally considered to be intractable for large models. The intractability of these algorithms is to a large extent a consequence of computing an exact, optimal policy over the entire belief space. However, in real-world POMDP problems, computing the optimal policy for the full belief space is often unnecessary for good control even for problems with complicated policy classes. The beliefs experienced by the controller often lie near a structured, low-dimensional subspace embedded in the high-dimensional belief space. Finding a good approximation to the optimal value function for only this subspace can be much easier than computing the full value function.#R##N##R##N#We introduce a new method for solving large-scale POMDPs by reducing the dimensionality of the belief space. We use Exponential family Principal Components Analysis (Collins, Dasgupta, & Schapire, 2002) to represent sparse, high-dimensional belief spaces using small sets of learned features of the belief state. We then plan only in terms of the low-dimensional belief features. By planning in this low-dimensional space, we can find policies for POMDP models that are orders of magnitude larger than models that can be handled by conventional techniques.#R##N##R##N#We demonstrate the use of this algorithm on a synthetic problem and on mobile robot navigation tasks.

Connected through hyperedge HE-1 (induced by source paper "a hamilton jacobi reachability based framework for predicting and analyzing human motion for safe planning"):
  with members:
    - "human motion trajectory prediction a survey"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "safe motion planning in unknown environments optimality benchmarks and tractable policies"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "hamilton jacobi reachability a brief overview and recent advances"
    - "multimodal probabilistic model based planning for human robot interaction"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "forecasting interactive dynamics of pedestrians with fictitious play"

Connected through hyperedge HE-2 (induced by source paper "efficient decision making and belief space planning using sparse approximations"):
  with members:
    - "complexity analysis and efficient measurement selection primitives for high rate graph slam"
    - "approximate planning for factored pomdps using belief state simplification"
    - "tractable inference for complex stochastic processes"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "compact representation of value function in partially observable stochastic games"):
  with members:
    - "heuristic search value iteration for pomdps"

Connected through hyperedge HE-4 (induced by source paper "on the cluster admission problem for cloud computing"):
  with members:
    - "overcommitment in cloud services bin packing with chance constraints"
    - "era a framework for economic resource allocation for the cloud"
    - "truthful online scheduling with commitments"
    - "fixed and market pricing for cloud services"
    - "fair allocation without trade"
    - "no justified complaints on fair sharing of multiple resources"
    - "nonapproximability results for partially observable markov decision processes"

Connected through hyperedge HE-5 (induced by source paper "permissive finite state controllers of pomdps using parameter synthesis"):
  with members:
    - "parametric markov chains pctl complexity and fraction free gaussian elimination"
    - "sequential convex programming for the efficient verification of parametric mdps"
    - "a storm is coming a modern probabilistic model checker"
    - "parameter synthesis for markov models faster than ever"
    - "safety constrained reinforcement learning for mdps"
    - "permissive controller synthesis for probabilistic systems"
    - "a symbolic sat based algorithm for almost sure reachability with small strategies in pomdps"
    - "optimal cost almost sure reachability in pomdps"

Connected through hyperedge HE-6 (induced by source paper "multi modal active perception for information gathering in science missions"):
  with members:
    - "data driven learning and planning for environmental sampling"
    - "an approach to autonomous science by modeling geological knowledge in a bayesian framework"
    - "modeling curiosity in a mobile robot for long term autonomous exploration and monitoring"
    - "loopy belief propagation for approximate inference an empirical study"
    - "learning bayesian networks the combination of knowledge and statistical data"

Connected through hyperedge HE-7 (induced by source paper "exploiting causality for selective belief filtering in dynamic bayesian networks"):
  with members:
    - "vector space analysis of belief state approximation for pomdps"
    - "causal independence for knowledge acquisition and inference"
    - "tractable inference for complex stochastic processes"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "context specific independence in bayesian networks"
    - "d separation from theorems to algorithms"
    - "the factored frontier algorithm for approximate inference in dbns"

Connected through hyperedge HE-8 (induced by source paper "making decisions with spatially and temporally uncertain data"):
  with members:
    - "symbolic dynamic programming for discrete and continuous state mdps"
    - "decision theoretic planning structural assumptions and computational leverage"
    - "reinforcement learning a survey"

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.AI(Artificial Intelligence)