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: "computational issues in time inconsistent planning"
  Abstract: Time-inconsistency refers to a paradox in decision making where agents exhibit inconsistent behaviors over time. Examples are procrastination where agents tends to costly postpone easy tasks, and abandonments where agents start a plan and quit in the middle. These behaviors are undesirable in the sense that agents make clearly suboptimal decisions over optimal ones. To capture such behaviors and more importantly, to quantify inefficiency caused by such behaviors, [Kleinberg & Oren 2014] propose a graph model which is essentially same as the standard planning model except for the cost structure. Using this model, they initiate the study of several interesting problems: 1) cost ratio: the worst ratio between the actual cost of the agent and the optimal cost, over all graph instances; 2) motivating subgraph: how to motivate the agent to reach the goal by deleting nodes and edges; 3) Intermediate rewards: how to motivate agents to reach the goal by placing intermediate rewards. Kleinberg and Oren give partial answers to these questions, but the main problems are still open. In fact, they raise these problems explicitly as open problems in their paper. In this paper, we give answers to all three open problems in [Kleinberg & Oren 2014]. First, we show a tight upper bound of cost ratio for graphs without Akerlof's structure, thus confirm the conjecture by Kleinberg and Oren that Akerlof's structure is indeed the worst case for cost ratio. Second, we prove that finding a motivating subgraph is NP-hard, showing that it is generally inefficient to motivate agents by deleting nodes and edges in the graph. Last but not least, we show that computing a strategy to place minimum amount of total reward is also NP-hard. Therefore, it is computational inefficient to motivate agents by placing intermediate rewards. The techniques we use to prove these results are nontrivial and of independent interests.

Connected through hyperedge HE-1 (induced by source paper "combinatorial auctions with endowment effect"):
  with members:
    - "the communication complexity of local search"
    - "planning with multiple biases"
    - "on the value of penalties in time inconsistent planning"
    - "the price of uncertainty in present biased planning"
    - "motivating time inconsistent agents a computational approach"
    - "procrastination with variable present bias"
    - "planning problems for sophisticated agents with present bias"
    - "time inconsistent planning a computational problem in behavioral economics"

Connected through hyperedge HE-2 (induced by source paper "directed graph embeddings"):
  with members:
    - "informational braess paradox the effect of information on traffic congestion"
    - "asymptotically tight bounds for inefficiency in risk averse selfish routing"
    - "playing the wrong game smoothness bounds for congestion games with heterogeneous biases"
    - "time inconsistent planning a computational problem in behavioral economics"
    - "a complete anytime algorithm for treewidth"

Connected through hyperedge HE-3 (induced by source paper "motivating time inconsistent agents a computational approach"):
  with members:
    - "planning with multiple biases"
    - "on the value of penalties in time inconsistent planning"
    - "the price of uncertainty in present biased planning"
    - "procrastination with variable present bias"
    - "planning problems for sophisticated agents with present bias"
    - "time inconsistent planning a computational problem in behavioral economics"

Connected through hyperedge HE-4 (induced by source paper "time inconsistent planning a computational problem in behavioral economics"):
  with members:
    - "planning with multiple biases"
    - "on the value of penalties in time inconsistent planning"
    - "motivating time inconsistent agents a computational approach"
    - "procrastination with variable present bias"
    - "planning problems for sophisticated agents with present bias"

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)