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: "the computational complexity of dominance and consistency in cp nets"
  Abstract: We investigate the computational complexity of testing dominance and consistency in CP-nets. Previously, the complexity of dominance has been determined for restricted classes in which the dependency graph of the CP-net is acyclic. However, there are preferences of interest that define cyclic dependency graphs; these are modeled with general CP-nets. In our main results, we show here that both dominance and consistency for general CP-nets are PSPACE-complete. We then consider the concept of strong dominance, dominance equivalence and dominance incomparability, and several notions of optimality, and identify the complexity of the corresponding decision problems. The reductions used in the proofs are from STRIPS planning, and thus reinforce the earlier established connections between both areas.

Connected through hyperedge HE-1 (induced by source paper "complexity results for preference aggregation over m cp nets pareto and majority voting"):
  with members:
    - "achieving new upper bounds for the hypergraph duality problem through logic"
    - "on the complexity of core kernel and bargaining set"
    - "non transferable utility coalitional games via mixed integer linear constraints"
    - "planning with preferences using logic programming"
    - "cp nets a tool for representing and reasoning with conditional ceteris paribus preference statements"

Connected through hyperedge HE-2 (induced by source paper "cpdist deep siamese networks for learning distances between structured preferences"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "understanding data augmentation for classification when to warp"
    - "research priorities for robust and beneficial artificial intelligence"
    - "deep convolutional networks on graph structured data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "the complexity of learning acyclic conditional preference networks"):
  with members:
    - "labeled compression schemes for extremal classes"
    - "a geometric approach to sample compression"
    - "cp nets a tool for representing and reasoning with conditional ceteris paribus preference statements"

Connected through hyperedge HE-4 (induced by source paper "solution dominance over constraint satisfaction problems"):
  with members:
    - "cp nets a tool for representing and reasoning with conditional ceteris paribus preference statements"

Connected through hyperedge HE-5 (induced by source paper "rank pruning for dominance queries in cp nets"):
  with members:
    - "ucp networks a directed graphical representation of conditional utilities"
    - "reasoning about soft constraints and conditional preferences complexity results and approximation techniques"
    - "cp nets a tool for representing and reasoning with conditional ceteris paribus preference statements"

Connected through hyperedge HE-6 (induced by source paper "encoding monotonic multi set preferences using ci nets preliminary report"):
  with members:
    - "on graphical modeling of preference and importance"
    - "cp nets a tool for representing and reasoning with conditional ceteris paribus preference statements"

Connected through hyperedge HE-7 (induced by source paper "logical conditional preference theories"):
  with members:
    - "xsb extending prolog with tabled logic programming"
    - "reasoning about soft constraints and conditional preferences complexity results and approximation techniques"
    - "cp nets a tool for representing and reasoning with conditional ceteris paribus preference statements"

Connected through hyperedge HE-8 (induced by source paper "crisner a practically efficient reasoner for qualitative preferences"):
  with members:
    - "representing and reasoning with qualitative preferences for compositional systems"
    - "on graphical modeling of preference and importance"
    - "cp nets a tool for representing and reasoning with conditional ceteris paribus preference statements"

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)