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"
  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 (i) focusing on classes of games where the Shapley value can be computed efficiently, or (ii) proposing representation formalisms that facilitate such efficient computation, or (iii) approximating the Shapley value in certain classes of games. For 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 (i) the \textit{variance}, and (ii) 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 $O(\frac{r}{m})$ to $O(\sqrt{\frac{r}{m}})$). Finally, we propose, and demonstrate the effectiveness of using stratified sampling for improving the bounds further.

Connected through hyperedge HE-1 (induced by source paper "efficient task specific data valuation for nearest neighbor algorithms"):
  with members:
    - "data markets to support ai for all pricing valuation and governance"
    - "towards efficient data valuation based on the shapley value"
    - "model based pricing for machine learning in a data marketplace"
    - "databright towards a global exchange for decentralized data ownership and trusted computation"
    - "learning non discriminatory predictors"
    - "equality of opportunity in supervised learning"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-2 (induced by source paper "the explanation game explaining machine learning models with cooperative game theory"):
  with members:
    - "explaining deep neural networks with a polynomial time algorithm for shapley values approximation"
    - "the many shapley values for model explanation"
    - "consistent individualized feature attribution for tree ensembles"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "l shapley and c shapley efficient model interpretation for structured data"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-3 (induced by source paper "who s responsible jointly quantifying the contribution of the learning algorithm and training data"):
  with members:
    - "towards efficient data valuation based on the shapley value"
    - "multiaccuracy black box post processing for fairness in classification"
    - "consistent individualized feature attribution for tree ensembles"
    - "l shapley and c shapley efficient model interpretation for structured data"
    - "a marketplace for data an algorithmic solution"
    - "a unified approach to interpreting model predictions"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-4 (induced by source paper "towards efficient data valuation based on the shapley value"):
  with members:
    - "finding influential training samples for gradient boosted decision trees"
    - "a unified approach to interpreting model predictions"
    - "towards evaluating the robustness of neural networks"

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)