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: "local utility elicitation in gai models"
  Abstract: Structured utility models are essential for the effective representation and elicitation of complex multiattribute utility functions. Generalized additive independence (GAI) models provide an attractive structural model of user preferences, offering a balanced tradeoff between simplicity and applicability. While representation and inference with such models is reasonably well understood, elicitation of the parameters of such models has been studied less from a practical perspective. We propose a procedure to elicit GAI model parameters using only "local" utility queries rather than "global" queries over full outcomes. Our local queries take full advantage of GAI structure and provide a sound framework for extending the elicitation procedure to settings where the uncertainty over utility parameters is represented probabilistically. We describe experiments using a myopic value-of-information approach to elicitation in a large GAI model.

Connected through hyperedge HE-1 (induced by source paper "gradient based optimization for bayesian preference elicitation"):
  with members:
    - "a cost effective framework for preference elicitation and aggregation"
    - "constructive preference elicitation over hybrid combinatorial spaces"
    - "adam a method for stochastic optimization"
    - "ucp networks a directed graphical representation of conditional utilities"

Connected through hyperedge HE-2 (induced by source paper "directed expected utility networks"):
  with members:
    - "from influence diagrams to junction trees"
    - "expected utility networks"

Connected through hyperedge HE-3 (induced by source paper "active learning for autonomous intelligent agents exploration curiosity and interaction"):
  with members:
    - "multi class generalized binary search for active inverse reinforcement learning"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"
    - "selecting the state representation in reinforcement learning"
    - "gaussian processes for sample efficient reinforcement learning with rmax like exploration"
    - "variance based rewards for approximate bayesian reinforcement learning"
    - "near optimal nonmyopic value of information in graphical models"
    - "safe exploration in markov decision processes"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-4 (induced by source paper "multiattribute auctions based on generalized additive independence"):
  with members:
    - "graphical models for preference and utility"
    - "ucp networks a directed graphical representation of conditional utilities"

Connected through hyperedge HE-5 (induced by source paper "eliciting single peaked preferences using comparison queries"):
  with members:
    - "utility elicitation as a classification problem"
    - "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"

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.GT(Computer Science and Game Theory)