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: "anytime heuristic search"
  Abstract: We describe how to convert the heuristic search algorithm A* into an anytime algorithm that finds a sequence of improved solutions and eventually converges to an optimal solution. The approach we adopt uses weighted heuristic search to find an approximate solution quickly, and then continues the weighted search to find improved solutions as well as to improve a bound on the suboptimality of the current solution. When the time available to solve a search problem is limited or uncertain, this creates an anytime heuristic search algorithm that allows a flexible tradeoff between search time and solution quality. We analyze the properties of the resulting Anytime A* algorithm, and consider its performance in three domains; sliding-tile puzzles, STRIPS planning, and multiple sequence alignment. To illustrate the generality of this approach, we also describe how to transform the memory-efficient search algorithm Recursive Best-First Search (RBFS) into an anytime algorithm.

Connected through hyperedge HE-1 (induced by source paper "best first search algorithm for non convex sparse minimization"):
  with members:
    - "dual iterative hard thresholding from non convex sparse minimization to non smooth concave maximization"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "gradient hard thresholding pursuit for sparsity constrained optimization"
    - "a orthogonal matching pursuit best first search for compressed sensing signal recovery"

Connected through hyperedge HE-2 (induced by source paper "design space exploration as quantified satisfaction"):
  with members:
    - "depqbf 6 0 a search based qbf solver beyond traditional qcdcl"
    - "twenty five comparators is optimal when sorting nine inputs and twenty nine for ten"
    - "practical graph isomorphism ii"

Connected through hyperedge HE-3 (induced by source paper "action selection for mdps anytime ao vs uct"):
  with members:
    - "bandit algorithms for tree search"

Connected through hyperedge HE-4 (induced by source paper "search based motion planning for aggressive flight in se 3"):
  with members:
    - "search based motion planning for quadrotors using linear quadratic minimum time control"
    - "aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-5 (induced by source paper "a survey of motion planning and control techniques for self driving urban vehicles"):
  with members:
    - "route planning in transportation networks"
    - "optimal sampling based motion planning under differential constraints the driftless case"
    - "discrete and continuous probabilistic anticipation for autonomous robots in urban environments"
    - "sampling based algorithms for optimal motion planning"
    - "theta any angle path planning on grids"

Connected through hyperedge HE-6 (induced by source paper "an online mechanism for ridesharing in autonomous mobility on demand systems"):
  with members:
    - "the social dilemma of autonomous vehicles"
    - "managing autonomous mobility on demand systems for better passenger experience"
    - "incentive design for ridesharing with uncertainty"
    - "an anytime algorithm for optimal coalition structure generation"

Connected through hyperedge HE-7 (induced by source paper "a search algorithm for simplicial complexes"):
  with members:
    - "theta any angle path planning on grids"

Connected through hyperedge HE-8 (induced by source paper "evaluating anytime algorithms for learning optimal bayesian networks"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "theory refinement on bayesian networks"
    - "improving the scalability of optimal bayesian network learning with external memory frontier breadth first branch and bound search"
    - "bayesian network learning with cutting planes"
    - "a simple approach for finding the globally optimal bayesian network structure"
    - "an improved admissible heuristic for learning optimal bayesian networks"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"

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)