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: "bounding the estimation error of sampling based shapley value approximation with without stratifying"
  Abstract: The Shapley value is arguably the most central normative solution concept in cooperative game theory. It specifies a unique way in which the reward from cooperation can be "fairly" divided among players. While it has a wide range of real world applications, its use is in many cases hampered by the hardness of its computation. A number of researchers have tackled this problem by (1) focusing on classes of games where the Shapley value can be computed efficiently, or (2) proposing representation formalisms that facilitate such efficient computation, or (3) approximating the Shapley value in certain classes of games. However, given the classical \textit{characteristic function} representation, the only attempt to approximate the Shapley value for the general class of games is due to Castro \textit{et al.} \cite{castro}. While this algorithm provides a bound on the approximation error, this bound is \textit{asymptotic}, meaning that it only holds when the number of samples increases to infinity. On the other hand, when a finite number of samples is drawn, an unquantifiable error is introduced, meaning that the bound no longer holds. With this in mind, we provide non-asymptotic bounds on the estimation error for two cases: where (1) the \textit{variance}, and (2) the \textit{range}, of the players' marginal contributions is known. Furthermore, for the second case, we show that when the range is significantly large relative to the Shapley value, the bound can be improved (from $O(r,\sqrt{\nicefrac{1}{m}})$ to $O(\sqrt{r},\sqrt{\nicefrac{1}{m}})$). Finally, we propose, and demonstrate the effectiveness of, using stratified sampling to improve the bounds.

Connected through hyperedge HE-1 (induced by source paper "the shapley taylor interaction index"):
  with members:
    - "recovering pairwise interactions using neural networks"
    - "hierarchical interpretations for neural network predictions"
    - "consistent individualized feature attribution for tree ensembles"
    - "did the model understand the question"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "axiomatic characterization of data driven influence measures for classification"

Connected through hyperedge HE-2 (induced by source paper "monte carlo techniques for approximating the myerson value theoretical and empirical analysis"):
  with members:
    - "efficient computation of the shapley value for game theoretic network centrality"

Connected through hyperedge HE-3 (induced by source paper "a marketplace for data an algorithmic solution"):
  with members:
    - "introduction to online convex optimization"
    - "adversarially robust generalization requires more data"
    - "statistical cost sharing"
    - "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"
    - "optimal mechanisms for selling information"

Connected through hyperedge HE-4 (induced by source paper "a study of proxies for shapley allocations of transport costs"):
  with members:
    - "shapley meets shapley"
    - "efficient computation of the shapley value for game theoretic network centrality"

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)