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: "weak calibration is computationally hard"
  Abstract: We show that the existence of a computationally efficient calibration algorithm, with a low weak calibration rate, would imply the existence of an efficient algorithm for computing approximate Nash equilibria - thus implying the unlikely conclusion that every problem in PPAD is solvable in polynomial time.

Connected through hyperedge HE-1 (induced by source paper "reliable confidence estimation via online learning"):
  with members:
    - "posterior calibration and exploratory analysis for natural language processing models"
    - "predicting accurate probabilities with a ranking loss"

Connected through hyperedge HE-2 (induced by source paper "scalable multilabel prediction via randomized methods"):
  with members:
    - "fast label embeddings for extremely large output spaces"
    - "comparing published multi label classifier performance measures to the ones obtained by a simple multi label baseline classifier"
    - "logarithmic time online multiclass prediction"
    - "efficient exact gradient update for training deep networks with very large sparse targets"
    - "fastfood approximate kernel expansions in loglinear time"
    - "least squares revisited scalable approaches for multi class prediction"
    - "simple and deterministic matrix sketching"
    - "multi label prediction via compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "query complexity of approximate nash equilibria"):
  with members:
    - "the query complexity of correlated equilibria"
    - "inapproximability of nash equilibrium"
    - "computational complexity of approximate nash equilibrium in large games"
    - "finding approximate nash equilibria of bimatrix games via payoff queries"
    - "learning equilibria of games via payoff queries"
    - "approximate nash equilibria via sampling"
    - "query complexity of correlated equilibrium"
    - "polynomial time computation of exact correlated equilibrium in compact games"

Connected through hyperedge HE-4 (induced by source paper "response based approachability and its application to generalized no regret algorithms"):
  with members:
    - "calibration and internal no regret with partial monitoring"

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)