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: "approximate pure nash equilibria in weighted congestion games existence efficient computation and structure"
  Abstract: We consider structural and algorithmic questions related to the Nash dynamics of weighted congestion games. In weighted congestion games with linear latency functions, the existence of (pure Nash) equilibria is guaranteed by potential function arguments. Unfortunately, this proof of existence is inefficient and computing equilibria is such games is a {\sf PLS}-hard problem. The situation gets worse when superlinear latency functions come into play; in this case, the Nash dynamics of the game may contain cycles and equilibria may not even exist. Given these obstacles, we consider approximate equilibria as alternative solution concepts. Do such equilibria exist? And if so, can we compute them efficiently? #R##N#We provide positive answers to both questions for weighted congestion games with polynomial latency functions by exploiting an "approximation" of such games by a new class of potential games that we call $\Psi$-games. This allows us to show that these games have $d!$-approximate equilibria, where $d$ is the maximum degree of the latency functions. Our main technical contribution is an efficient algorithm for computing O(1)-approximate equilibria when $d$ is a constant. For games with linear latency functions, the approximation guarantee is $\frac{3+\sqrt{5}}{2}+O(\gamma)$ for arbitrarily small $\gamma>0$; for latency functions with maximum degree $d\geq 2$, it is $d^{2d+o(d)}$. The running time is polynomial in the number of bits in the representation of the game and $1/\gamma$. As a byproduct of our techniques, we also show the following structural statement for weighted congestion games with polynomial latency functions of maximum degree $d\geq 2$: polynomially-long sequences of best-response moves from any initial state to a $d^{O(d^2)}$-approximate equilibrium exist and can be efficiently identified in such games as long as $d$ is constant.

Connected through hyperedge HE-1 (induced by source paper "existence and complexity of approximate equilibria in weighted congestion games"):
  with members:
    - "on approximate pure nash equilibria in weighted congestion games with polynomial latencies"
    - "an improved algorithm for computing approximate equilibria in weighted congestion games"
    - "the price of stability of weighted congestion games"
    - "computing approximate pure nash equilibria in shapley value weighted congestion games"
    - "efficient computation of approximate pure nash equilibria in congestion games"
    - "on the performance of approximate equilibria in congestion games"

Connected through hyperedge HE-2 (induced by source paper "on approximate pure nash equilibria in weighted congestion games with polynomial latencies"):
  with members:
    - "the price of stability of weighted congestion games"
    - "computing approximate pure nash equilibria in shapley value weighted congestion games"
    - "efficient computation of approximate pure nash equilibria in congestion games"
    - "near rationality and competitive equilibria in networked systems"

Connected through hyperedge HE-3 (induced by source paper "node max cut and computing equilibria in linear weighted congestion games"):
  with members:
    - "computing approximate pure nash equilibria in shapley value weighted congestion games"
    - "local max cut in smoothed polynomial time"
    - "efficient computation of approximate pure nash equilibria in congestion games"

Connected through hyperedge HE-4 (induced by source paper "an improved algorithm for computing approximate equilibria in weighted congestion games"):
  with members:
    - "the price of stability of weighted congestion games"
    - "computing approximate pure nash equilibria in shapley value weighted congestion games"
    - "efficient computation of approximate pure nash equilibria in congestion games"
    - "a unifying tool for bounding the quality of non cooperative solutions in weighted congestion games"
    - "on the performance of approximate equilibria in congestion games"

Connected through hyperedge HE-5 (induced by source paper "the price of stability of weighted congestion games"):
  with members:
    - "computing approximate pure nash equilibria in shapley value weighted congestion games"
    - "efficient computation of approximate pure nash equilibria in congestion games"
    - "a unifying tool for bounding the quality of non cooperative solutions in weighted congestion games"
    - "on the performance of approximate equilibria in congestion games"

Connected through hyperedge HE-6 (induced by source paper "congestion games with complementarities"):
  with members:
    - "congestion games with mixed objectives"
    - "short sequences of improvement moves lead to approximate equilibria in constraint satisfaction games"
    - "efficient computation of approximate pure nash equilibria in congestion games"
    - "on the performance of approximate equilibria in congestion games"

Connected through hyperedge HE-7 (induced by source paper "computing approximate pure nash equilibria in shapley value weighted congestion games"):
  with members:
    - "sharing non anonymous costs of multiple resources optimally"
    - "potential games are necessary to ensure pure nash equilibria in cost sharing games"
    - "shapley meets shapley"
    - "efficient computation of approximate pure nash equilibria in congestion games"

Connected through hyperedge HE-8 (induced by source paper "reconciling selfish routing with social good"):
  with members:
    - "informational braess paradox the effect of information on traffic congestion"
    - "mechanism design in large games incentives and privacy"
    - "on the performance of approximate equilibria in congestion games"

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)