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: "planning problems for sophisticated agents with present bias"
  Abstract: Present bias, the tendency to weigh costs and benefits incurred in the present too heavily, is one of the most widespread human behavioral biases. It has also been the subject of extensive study in the behavioral economics literature. While the simplest models assume that the agents are naive, reasoning about the future without taking their bias into account, there is considerable evidence that people often behave in ways that are sophisticated with respect to present bias, making plans based on the belief that they will be present-biased in the future. For example, committing to a course of action to reduce future opportunities for procrastination or overconsumption are instances of sophisticated behavior in everyday life. #R##N#Models of sophisticated behavior have lacked an underlying formalism that allows one to reason over the full space of multi-step tasks that a sophisticated agent might face. This has made it correspondingly difficult to make comparative or worst-case statements about the performance of sophisticated agents in arbitrary scenarios. In this paper, we incorporate the notion of sophistication into a graph-theoretic model that we used in recent work for modeling naive agents. This new synthesis of two formalisms - sophistication and graph-theoretic planning - uncovers a rich structure that wasn't apparent in the earlier behavioral economics work on this problem. #R##N#In particular, our graph-theoretic model makes two kinds of new results possible. First, we give tight worst-case bounds on the performance of sophisticated agents in arbitrary multi-step tasks relative to the optimal plan. Second, the flexibility of our formalism makes it possible to identify new phenomena that had not been seen in prior literature: these include a surprising non-monotonic property in the use of rewards to motivate sophisticated agents and a framework for reasoning about commitment devices.

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"
    - "time inconsistent planning a computational problem in behavioral economics"

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"
    - "contingent payment mechanisms to maximize resource utilization"
    - "time inconsistent planning a computational problem in behavioral economics"

Connected through hyperedge HE-3 (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"
    - "computational issues in time inconsistent planning"
    - "time inconsistent planning a computational problem in behavioral economics"

Connected through hyperedge HE-4 (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"
    - "time inconsistent planning a computational problem in behavioral economics"

Connected through hyperedge HE-5 (induced by source paper "dancing pigs or externalities measuring the rationality of security decisions"):
  with members:
    - "planning with multiple biases"
    - "procrastination with variable present bias"
    - "your browsing behavior for a big mac economics of personal information online"
    - "near rationality and competitive equilibria in networked systems"

Connected through hyperedge HE-6 (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"
    - "time inconsistent planning a computational problem in behavioral economics"

Connected through hyperedge HE-7 (induced by source paper "planning with multiple biases"):
  with members:
    - "motivating time inconsistent agents a computational approach"
    - "procrastination with variable present bias"
    - "time inconsistent planning a computational problem in behavioral economics"

Connected through hyperedge HE-8 (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"
    - "computational issues in time inconsistent planning"
    - "time inconsistent planning a computational problem in behavioral economics"

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)