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: "utility elicitation as a classification problem"
  Abstract: We investigate the application of classification techniques to utility elicitation. In a decision problem, two sets of parameters must generally be elicited: the probabilities and the utilities. While the prior and conditional probabilities in the model do not change from user to user, the utility models do. Thus it is necessary to elicit a utility model separately for each new user. Elicitation is long and tedious, particularly if the outcome space is large and not decomposable. There are two common approaches to utility function elicitation. The first is to base the determination of the users utility function solely ON elicitation OF qualitative preferences.The second makes assumptions about the form AND decomposability OF the utility function.Here we take a different approach: we attempt TO identify the new USERs utility function based on classification relative to a database of previously collected utility functions. We do this by identifying clusters of utility functions that minimize an appropriate distance measure. Having identified the clusters, we develop a classification scheme that requires many fewer and simpler assessments than full utility elicitation and is more robust than utility elicitation based solely on preferences. We have tested our algorithm on a small database of utility functions in a prenatal diagnosis domain and the results are quite promising.

Connected through hyperedge HE-1 (induced by source paper "local utility elicitation in gai models"):
  with members:
    - "graphical models for preference and utility"
    - "ucp networks a directed graphical representation of conditional utilities"

Connected through hyperedge HE-2 (induced by source paper "toward experiential utility elicitation for interface customization"):
  with members:
    - "the lumiere project bayesian user modeling for inferring the goals and needs of software users"

Connected through hyperedge HE-3 (induced by source paper "eliciting single peaked preferences using comparison queries"):
  with members:
    - "local utility elicitation in gai models"
    - "dichotomy for voting systems"
    - "vote elicitation complexity and strategy proofness"
    - "exact complexity of the winner problem for young elections"
    - "learning to order things"
    - "exact analysis of dodgson elections lewis carroll s 1876 voting system is complete for parallel access to np"

Connected through hyperedge HE-4 (induced by source paper "cp nets a tool for representing and reasoning with conditional ceteris paribus preference statements"):
  with members:
    - "graphical models for preference and utility"
    - "expected utility networks"
    - "ucp networks a directed graphical representation of conditional utilities"
    - "conditional utility utility independence and utility networks"
    - "reasoning about soft constraints and conditional preferences complexity results and approximation techniques"
    - "structure and complexity in planning with unary operators"

Connected through hyperedge HE-5 (induced by source paper "sapa a multi objective metric temporal planner"):
  with members:
    - "pddl2 1 an extension to pddl for expressing temporal planning domains"
    - "vhpop versatile heuristic partial order planner"
    - "the 3rd international planning competition results and analysis"
    - "the ff planning system fast plan generation through heuristic search"
    - "the grt planning system backward heuristic construction in forward state space planning"
    - "efficient implementation of the plan graph in stan"

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)