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: "time inconsistent planning a computational problem in behavioral economics"
  Abstract: In many settings, people exhibit behavior that is inconsistent across time --- we allocate a block of time to get work done and then procrastinate, or put effort into a project and then later fail to complete it. An active line of research in behavioral economics and related fields has developed and analyzed models for this type of time-inconsistent behavior. #R##N#Here we propose a graph-theoretic model of tasks and goals, in which dependencies among actions are represented by a directed graph, and a time-inconsistent agent constructs a path through this graph. We first show how instances of this path-finding problem on different input graphs can reconstruct a wide range of qualitative phenomena observed in the literature on time-inconsistency, including procrastination, abandonment of long-range tasks, and the benefits of reduced sets of choices. We then explore a set of analyses that quantify over the set of all graphs; among other results, we find that in any graph, there can be only polynomially many distinct forms of time-inconsistent behavior; and any graph in which a time-inconsistent agent incurs significantly more cost than an optimal agent must contain a large "procrastination" structure as a minor. Finally, we use this graph-theoretic model to explore ways in which tasks can be designed to help motivate agents to reach designated goals.

Connected through hyperedge HE-1 (induced by source paper "time inconsistent planning simple motivation is hard to find"):
  with members:
    - "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"
    - "planning problems for sophisticated agents with present bias"

Connected through hyperedge HE-2 (induced by source paper "penalty bidding mechanisms for allocating resources and overcoming present bias"):
  with members:
    - "planning with multiple biases"
    - "contingent payment mechanisms for resource utilization"
    - "procrastination with variable present bias"
    - "planning problems for sophisticated agents with present bias"
    - "contingent payment mechanisms to maximize resource utilization"

Connected through hyperedge HE-3 (induced by source paper "directed graph minors and serial parallel width"):
  with members:
    - "asymptotically tight bounds for inefficiency in risk averse selfish routing"
    - "the burden of risk aversion in mean risk selfish routing"
    - "a complete anytime algorithm for treewidth"

Connected through hyperedge HE-4 (induced by source paper "monopoly pricing with buyer search"):
  with members:
    - "planning with multiple biases"
    - "on the value of penalties in time inconsistent planning"
    - "revenue gaps for discriminatory and anonymous sequential posted pricing"
    - "the menu size complexity of revenue approximation"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "sampling and representation complexity of revenue maximization"
    - "on the complexity of dynamic mechanism design"
    - "combinatorial auctions via posted prices"

Connected through hyperedge HE-5 (induced by source paper "where do you think you re going inferring beliefs about dynamics from behavior"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning models for shared control of human machine systems with unknown dynamics"
    - "shared autonomy via deep reinforcement learning"
    - "planning fast and slow a framework for adaptive real time safe trajectory planning"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "impossibility of deducing preferences and rationality from human policy"
    - "reinforcement learning with deep energy based policies"

Connected through hyperedge HE-6 (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"
    - "computational issues in time inconsistent planning"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning and inverse reinforcement learning with system 1 and system 2"):
  with members:
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "consequentialist conditional cooperation in social dilemmas with imperfect information"
    - "cooperative inverse reinforcement learning"
    - "planning problems for sophisticated agents with present bias"

Connected through hyperedge HE-8 (induced by source paper "the price of uncertainty in present biased planning"):
  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)