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: "conformant planning via symbolic model checking"
  Abstract: We tackle the problem of planning in nondeterministic domains, by presenting a new approach to conformant planning. Conformant planning is the problem of finding a sequence of actions that is guaranteed to achieve the goal despite the nondeterminism of the domain. Our approach is based on the representation of the planning domain as a finite state automaton. We use Symbolic Model Checking techniques, in particular Binary Decision Diagrams, to compactly represent and efficiently search the automaton. In this paper we make the following contributions. First, we present a general planning algorithm for conformant planning, which applies to fully nondeterministic domains, with uncertainty in the initial condition and in action effects. The algorithm is based on a breadth-first, backward search, and returns conformant plans of minimal length, if a solution to the planning problem exists, otherwise it terminates concluding that the problem admits no conformant solution. Second, we provide a symbolic representation of the search space based on Binary Decision Diagrams (BDDs), which is the basis for search techniques derived from symbolic model checking. The symbolic representation makes it possible to analyze potentially large sets of states and transitions in a single computation step, thus providing for an efficient implementation. Third, we present CMBP (Conformant Model Based Planner), an efficient implementation of the data structures and algorithm described above, directly based on BDD manipulations, which allows for a compact representation of the search layers and an efficient implementation of the search steps. Finally, we present an experimental comparison of our approach with the state-of-the-art conformant planners CGP, QBFPLAN and GPT. Our analysis includes all the planning problems from the distribution packages of these systems, plus other problems defined to stress a number of specific factors. Our approach appears to be the most effective: CMBP is strictly more expressive than QBFPLAN and CGP and, in all the problems where a comparison is possible, CMBP outperforms its competitors, sometimes by orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "a dynamic epistemic framework for conformant planning"):
  with members:
    - "compiling uncertainty away in conformant planning problems with bounded width"
    - "planning graph heuristics for belief space search"

Connected through hyperedge HE-2 (induced by source paper "reduced ordered binary decision diagram with implied literals a new knowledge compilation approach"):
  with members:
    - "the language of search"
    - "a knowledge compilation map"

Connected through hyperedge HE-3 (induced by source paper "qualitative mdps and pomdps an order of magnitude approximation"):
  with members:
    - "a possibilistic model for qualitative sequential decision problems under uncertainty in partially observable environments"
    - "an order of magnitude calculus"
    - "a qualitative linear utility theory for spohn s theory of epistemic beliefs"
    - "a general non probabilistic theory of inductive reasoning"
    - "from conditional oughts to qualitative decision theory"
    - "value function approximations for partially observable markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "replanning in domains with partial information and sensing actions"):
  with members:
    - "heuristic search value iteration for pomdps"
    - "compiling uncertainty away in conformant planning problems with bounded width"
    - "planning graph heuristics for belief space search"
    - "learning to coordinate efficiently a model based approach"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-5 (induced by source paper "planning under continuous time and resource uncertainty a challenge for ai"):
  with members:
    - "constructing conditional plans by a theorem prover"
    - "the computational complexity of probabilistic planning"

Connected through hyperedge HE-6 (induced by source paper "reasoning and planning with sensing actions incomplete information and static causal laws using answer set programming"):
  with members:
    - "planning graph heuristics for belief space search"
    - "a logic programming approach to knowledge state planning ii the dlv k system"
    - "domain dependent knowledge in answer set planning"
    - "the ff planning system fast plan generation through heuristic search"
    - "constructing conditional plans by a theorem prover"
    - "stable models and an alternative logic programming paradigm"

Connected through hyperedge HE-7 (induced by source paper "planning graph heuristics for belief space search"):
  with members:
    - "the 3rd international planning competition results and analysis"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-8 (induced by source paper "towards automated integration of guess and check programs in answer set programming a meta interpreter and applications"):
  with members:
    - "the dlv system for knowledge representation and reasoning"
    - "an abductive framework for computing knowledge base updates"
    - "on the expressibility of stable logic programming"
    - "diagnostic reasoning with a prolog"
    - "a logic programming approach to knowledge state planning ii the dlv k system"
    - "computing preferred answer sets by meta interpretation in answer set programming"
    - "on properties of update sequences based on causal rejection"
    - "dlv a system for declarative problem solving"

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)