human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "the shapley taylor interaction index"):
  Abstract: The attribution problem, that is the problem of attributing a model's prediction to its base features, is well-studied. We extend the notion of attribution to also apply to feature interactions. #R##N#The Shapley value is a commonly used method to attribute a model's prediction to its base features. We propose a generalization of the Shapley value called Shapley-Taylor index that attributes the model's prediction to interactions of subsets of features up to some size k. The method is analogous to how the truncated Taylor Series decomposes the function value at a certain point using its derivatives at a different point. In fact, we show that the Shapley Taylor index is equal to the Taylor Series of the multilinear extension of the set-theoretic behavior of the model. #R##N#We axiomatize this method using the standard Shapley axioms -- linearity, dummy, symmetry and efficiency -- and an additional axiom that we call the interaction distribution axiom. This new axiom explicitly characterizes how interactions are distributed for a class of functions that model pure interaction. #R##N#We contrast the Shapley-Taylor index against the previously proposed Shapley Interaction index (cf. [9]) from the cooperative game theory literature. We also apply the Shapley Taylor index to three models and identify interesting qualitative insights.

Includes members:
  Member "recovering pairwise interactions using neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explaining explanations axiomatic feature interactions for deep networks")
  Member "hierarchical interpretations for neural network predictions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evaluating recurrent neural network explanations")
  Member "consistent individualized feature attribution for tree ensembles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generating interpretable poverty maps using object detection in satellite images")
  Member "did the model understand the question":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semantic robustness of models of source code")
  Member "axiomatic attribution for deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")
  Member "a unified approach to interpreting model predictions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "learning important features through propagating activation differences":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "importance driven deep learning system testing")
  Member "axiomatic characterization of data driven influence measures for classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "less is better unweighted data subsampling via influence function")

Question: Please tell me which class the source paper 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)