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: "motivating time inconsistent agents a computational approach"
  Abstract: In this paper we investigate the computational complexity of motivating time-inconsistent agents to complete long term projects. We resort to an elegant graph-theoretic model, introduced by Kleinberg and Oren, which consists of a task graph $G$ with $n$ vertices, including a source $s$ and target $t$, and an agent that incrementally constructs a path from $s$ to $t$ in order to collect rewards. The twist is that the agent is present-biased and discounts future costs and rewards by a factor $\beta\in [0,1]$. Our design objective is to ensure that the agent reaches $t$ i.e.\ completes the project, for as little reward as possible. Such graphs are called motivating. We consider two strategies. First, we place a single reward $r$ at $t$ and try to guide the agent by removing edges from $G$. We prove that deciding the existence of such motivating subgraphs is NP-complete if $r$ is fixed. More importantly, we generalize our reduction to a hardness of approximation result for computing the minimum $r$ that admits a motivating subgraph. In particular, we show that no polynomial-time approximation to within a ratio of $\sqrt{n}/4$ or less is possible, unless ${\rm P}={\rm NP}$. Furthermore, we develop a $(1+\sqrt{n})$-approximation algorithm and thus settle the approximability of computing motivating subgraphs. Secondly, we study motivating reward configurations, where non-negative rewards $r(v)$ may be placed on arbitrary vertices $v$ of $G$. The agent only receives the rewards of visited vertices. Again we give an NP-completeness result for deciding the existence of a motivating reward configuration within a fixed budget $b$. This result even holds if $b=0$, which in turn implies that no efficient approximation of a minimum $b$ within a ration grater or equal to $1$ is possible, unless ${\rm P}={\rm NP}$.

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"
    - "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 "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"
    - "procrastination with variable present bias"
    - "planning problems for sophisticated agents with present bias"
    - "computational issues in time inconsistent planning"
    - "time inconsistent planning a computational problem in behavioral economics"

Connected through hyperedge HE-3 (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"
    - "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 "planning with multiple biases"):
  with members:
    - "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-5 (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"
    - "procrastination with variable present bias"
    - "planning problems for sophisticated agents with present bias"
    - "computational issues in time inconsistent planning"

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.CC(Computational Complexity)