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: "statistical cost sharing"
  Abstract: We study the cost sharing problem for cooperative games in situations where the cost function $C$ is not available via oracle queries, but must instead be derived from data, represented as tuples $(S, C(S))$, for different subsets $S$ of players. We formalize this approach, which we call statistical cost sharing, and consider the computation of the core and the Shapley value, when the tuples are drawn from some distribution $\mathcal{D}$. #R##N#Previous work by Balcan et al. in this setting showed how to compute cost shares that satisfy the core property with high probability for limited classes of functions. We expand on their work and give an algorithm that computes such cost shares for any function with a non-empty core. We complement these results by proving an inapproximability lower bound for a weaker relaxation. #R##N#We then turn our attention to the Shapley value. We first show that when cost functions come from the family of submodular functions with bounded curvature, $\kappa$, the Shapley value can be approximated from samples up to a $\sqrt{1 - \kappa}$ factor, and that the bound is tight. We then define statistical analogues of the Shapley axioms, and derive a notion of statistical Shapley value. We show that these can always be approximated arbitrarily well for general functions over any distribution $\mathcal{D}$.

Connected through hyperedge HE-1 (induced by source paper "a learning framework for distribution based game theoretic solution concepts"):
  with members:
    - "forming probably stable communities with limited interactions"
    - "envy free classification"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "hedonic games with graph restricted communication"
    - "learning simple auctions"
    - "sample complexity of automated mechanism design"
    - "learning cooperative games"
    - "the sample complexity of auctions with side information"

Connected through hyperedge HE-2 (induced by source paper "a marketplace for data an algorithmic solution"):
  with members:
    - "introduction to online convex optimization"
    - "adversarially robust generalization requires more data"
    - "tensorflow a system for large scale machine learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "mllib machine learning in apache spark"
    - "learning in auctions regret is hard envy is easy"
    - "bounding the estimation error of sampling based shapley value approximation with without stratifying"
    - "optimal mechanisms for selling information"

Connected through hyperedge HE-3 (induced by source paper "a characterization of monotone influence measures for data classification"):
  with members:
    - "axiomatic attribution for deep networks"
    - "use privacy in data driven systems theory and experiments with machine learnt programs"
    - "learning important features through propagating activation differences"
    - "challenges for transparency"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "proxy non discrimination in data driven systems"
    - "why should i trust you explaining the predictions of any classifier"
    - "learning cooperative 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)