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: "axiomatic attribution for multilinear functions"
  Abstract: We study the attribution problem. That is, given a real-valued characteristic function f of n variables and initial and final values r and s for its independent variables, our objective is to divide the responsibility for the change f(s) - f(r) in the characteristic function among each of its independent variables. We call these assigned responsibilities attributions, and we would like the attributions to form a complete partition of the total change. When r=0, the attribution problem coincides with a standard cost sharing model from the social choice literature (cf. Moulin [2]), where the characteristic function is the cost function, the independent variables are the demands of the agents, and the attributions are cost shares for the agents. We follow the cost sharing literature in identifying good attribution methods axiomatically (for a classical example, see Friedman and Moulin [1]). We consider: Additivity -- attributions are additive in the characteristic function, Dummy -- if the characteristic function does not depend on a variable, then its attribution is zero, and Affine Scale Invariance -- attributions are invariant under simultaneous affine transformation of the characteristic function and the variables.   First, we show that when the characteristic function is the sum of a multilinear function and an additively separable one, every attribution method satisfying these axioms is a random order method. Intuitively, a multilinear function is determined by its values on the vertices of a hypercube, so its attributions should depend on these values alone, leading to the space of random order methods. The proof proceeds by using this idea to count dimensions.   Second, in our main result, we show that there is a unique attribution method satisfying these axioms and Anonymity (which requires attributions to be invariant under relabeling of the variables) if and only if the characteristic function is the sum of a multilinear function and an additively separable one. The main technical tool is the use of Stokes' Theorem to compare attribution methods. The resulting method coincides with the classical Aumann-Shapley and Shapley-Shubik methods, and thus we term it the Aumann-Shapley-Shubik method. When the characteristic function is multilinear, our result prescribes this method for use; to this end, we provide a computationally efficient implementation.   Together, our results single out the class of multilinear characteristic functions as a particularly nice one for attribution problems. We give several examples of natural attribution problems where such functions arise, including pay-per-click advertising, website traffic analysis, portfolio analysis, and performance analysis of sports teams.

Connected through hyperedge HE-1 (induced by source paper "explaining deep neural networks with a polynomial time algorithm for shapley values approximation"):
  with members:
    - "lightweight probabilistic deep networks"
    - "explanation methods in deep learning users values concerns and challenges"
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "sanity checks for saliency maps"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "visualizing deep neural network decisions prediction difference analysis"

Connected through hyperedge HE-2 (induced by source paper "the many shapley values for model explanation"):
  with members:
    - "a new interaction index inspired by the taylor series"
    - "consistent individualized feature attribution for tree ensembles"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-3 (induced by source paper "gradients of counterfactuals"):
  with members:
    - "understanding neural networks through deep visualization"
    - "inverting visual representations with convolutional networks"
    - "understanding deep image representations by inverting them"
    - "striving for simplicity the all convolutional net"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "building high level features using large scale unsupervised learning"

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)