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: "structure and complexity in planning with unary operators"
  Abstract: Unary operator domains - i.e., domains in which operators have a single effect - arise naturally in many control problems. In its most general form, the problem of STRIPS planning in unary operator domains is known to be as hard as the general STRIPS planning problem - both are PSPACE-complete. However, unary operator domains induce a natural structure, called the domain's causal graph. This graph relates between the preconditions and effect of each domain operator. Causal graphs were exploited by Williams and Nayak in order to analyze plan generation for one of the controllers in NASA's Deep-Space One spacecraft. There, they utilized the fact that when this graph is acyclic, a serialization ordering over any subgoal can be obtained quickly. In this paper we conduct a comprehensive study of the relationship between the structure of a domain's causal graph and the complexity of planning in this domain. On the positive side, we show that a non-trivial polynomial time plan generation algorithm exists for domains whose causal graph induces a polytree with a constant bound on its node indegree. On the negative side, we show that even plan existence is hard when the graph is a directed-path singly connected DAG. More generally, we show that the number of paths in the causal graph is closely related to the complexity of planning in the associated domain. Finally we relate our results to the question of complexity of planning with serializable subgoals.

Connected through hyperedge HE-1 (induced by source paper "a complete parameterized complexity analysis of bounded planning"):
  with members:
    - "parameterized complexity results for plan reuse"
    - "the complexity of planning revisited a parameterized analysis"
    - "solving max r sat above a tight lower bound"
    - "the complexity of planning problems with simple causal graphs"
    - "the fast downward planning system"

Connected through hyperedge HE-2 (induced by source paper "a refined view of causal graphs and component sizes sp closed graph classes and beyond"):
  with members:
    - "implicit abstraction heuristics"
    - "planning over chain causal graphs for variables with domains of size 5 is np hard"
    - "the complexity of planning problems with simple causal graphs"
    - "the fast downward planning system"

Connected through hyperedge HE-3 (induced by source paper "algorithms and limits for compact plan representations"):
  with members:
    - "on the size of data structures used in symbolic model checking"
    - "random access to grammar compressed strings"
    - "the complexity of planning problems with simple causal graphs"
    - "complexity issues in finding succinct solutions of pspace complete problems"
    - "on the complexity of case based planning"
    - "ordered landmarks in planning"
    - "on the compilability and expressive power of propositional planning formalisms"
    - "space efficiency of propositional knowledge representation formalisms"

Connected through hyperedge HE-4 (induced by source paper "the complexity of planning revisited a parameterized analysis"):
  with members:
    - "augmenting tractable fragments of abstract argumentation"
    - "the complexity of planning problems with simple causal graphs"
    - "where ignoring delete lists works local search topology in planning benchmarks"
    - "taming numbers and durations in the model checking integrated planning system"

Connected through hyperedge HE-5 (induced by source paper "rapid a reachable anytime planner for imprecisely sensed domains"):
  with members:
    - "point based pomdp algorithms improved analysis and implementation"

Connected through hyperedge HE-6 (induced by source paper "analyzing search topology without running any search on the connection between causal graphs and h"):
  with members:
    - "the lama planner guiding cost based anytime planning with landmarks"
    - "planning over chain causal graphs for variables with domains of size 5 is np hard"
    - "the complexity of planning problems with simple causal graphs"
    - "the fast downward planning system"
    - "where ignoring delete lists works local search topology in planning benchmarks"
    - "ordered landmarks in planning"
    - "planning through stochastic local search and temporal action graphs in lpg"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-7 (induced by source paper "planning over chain causal graphs for variables with domains of size 5 is np hard"):
  with members:
    - "the complexity of planning problems with simple causal graphs"
    - "the fast downward planning system"

Connected through hyperedge HE-8 (induced by source paper "on the fly macros"):
  with members:
    - "marvin a heuristic search planner with online macro action learning"
    - "the fast downward planning system"
    - "macro ff improving ai planning with automatically learned macro operators"
    - "the ff planning system fast plan generation through heuristic search"

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)