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: "graphical models for preference and utility"
  Abstract: Probabilistic independence can dramatically simplify the task of eliciting, representing, and computing with probabilities in large domains. A key technique in achieving these benefits is the idea of graphical modeling. We survey existing notions of independence for utility functions in a multi-attribute space, and suggest that these can be used to achieve similar advantages. Our new results concern conditional additive independence, which we show always has a perfect representation as separation in an undirected graph (a Markov network). Conditional additive independencies entail a particular functional for the utility function that is analogous to a product decomposition of a probability function, and confers analogous benefits. This functional form has been utilized in the Bayesian network and influence diagram literature, but generally without an explanation in terms of independence. The functional form yields a decomposition of the utility function that can greatly speed up expected utility calculations, particularly when the utility graph has a similar topology to the probabilistic network being used.

Connected through hyperedge HE-1 (induced by source paper "axiomatization of an importance index for k ary games"):
  with members:
    - "minimax regret based elicitation of generalized additive utilities"

Connected through hyperedge HE-2 (induced by source paper "on the decomposition of generalized additive independence models"):
  with members:
    - "ucp networks a directed graphical representation of conditional utilities"

Connected through hyperedge HE-3 (induced by source paper "topological parameters for time space tradeoff"):
  with members:
    - "a probabilistic approach to hierarchical model based diagnosis"
    - "nested junction trees"
    - "an evaluation of structural parameters for probabilistic reasoning results on benchmark circuits"
    - "optimal junction trees"
    - "global conditioning for probabilistic inference in belief networks"
    - "a sufficiently fast algorithm for finding close to optimal junction trees"

Connected through hyperedge HE-4 (induced by source paper "utilities as random variables density estimation and structure discovery"):
  with members:
    - "from influence diagrams to junction trees"
    - "context specific independence in bayesian networks"
    - "learning gaussian networks"

Connected through hyperedge HE-5 (induced by source paper "ucp networks a directed graphical representation of conditional utilities"):
  with members:
    - "expected utility networks"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-6 (induced by source paper "problem focused incremental elicitation of multi attribute utility models"):
  with members:
    - "elicitation of probabilities for belief networks combining qualitative and quantitative information"

Connected through hyperedge HE-7 (induced by source paper "unstructuring user preferences efficient non parametric utility revelation"):
  with members:
    - "expected utility networks"
    - "ucp networks a directed graphical representation of conditional utilities"
    - "a hybrid approach to reasoning with partially elicited preference models"
    - "compact value function representations for qualitative preferences"
    - "cp nets a tool for representing and reasoning with conditional ceteris paribus preference statements"

Connected through hyperedge HE-8 (induced by source paper "compact value function representations for qualitative preferences"):
  with members:
    - "ucp networks a directed graphical representation of conditional utilities"
    - "introducing variable importance tradeoffs into cp nets"
    - "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"

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)