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: "the inverse shapley value problem"
  Abstract: For $f$ a weighted voting scheme used by $n$ voters to choose between two candidates, the $n$ \emph{Shapley-Shubik Indices} (or {\em Shapley values}) of $f$ provide a measure of how much control each voter can exert over the overall outcome of the vote. Shapley-Shubik indices were introduced by Lloyd Shapley and Martin Shubik in 1954 \cite{SS54} and are widely studied in social choice theory as a measure of the "influence" of voters. The \emph{Inverse Shapley Value Problem} is the problem of designing a weighted voting scheme which (approximately) achieves a desired input vector of values for the Shapley-Shubik indices. Despite much interest in this problem no provably correct and efficient algorithm was known prior to our work. #R##N#We give the first efficient algorithm with provable performance guarantees for the Inverse Shapley Value Problem. For any constant $\eps > 0$ our algorithm runs in fixed poly$(n)$ time (the degree of the polynomial is independent of $\eps$) and has the following performance guarantee: given as input a vector of desired Shapley values, if any "reasonable" weighted voting scheme (roughly, one in which the threshold is not too skewed) approximately matches the desired vector of values to within some small error, then our algorithm explicitly outputs a weighted voting scheme that achieves this vector of Shapley values to within error $\eps.$ If there is a "reasonable" voting scheme in which all voting weights are integers at most $\poly(n)$ that approximately achieves the desired Shapley values, then our algorithm runs in time $\poly(n)$ and outputs a weighted voting scheme that achieves the target vector of Shapley values to within error $\eps=n^{-1/8}.$

Connected through hyperedge HE-1 (induced by source paper "degree d chow parameters robustly determine degree d ptfs and algorithmic applications"):
  with members:
    - "learning geometric concepts with nasty noise"
    - "hardness of learning noisy halfspaces using polynomial thresholds"
    - "robust estimators in high dimensions without the computational intractability"
    - "pseudorandomness via the discrete fourier transform"
    - "complexity theoretic limitations on learning halfspaces"
    - "super linear gate and super quadratic wire lower bounds for depth two and depth three threshold circuits"
    - "a ptas for agnostically learning halfspaces"
    - "analysis of boolean functions"

Connected through hyperedge HE-2 (induced by source paper "on the complexity of the inverse semivalue problem for weighted voting games"):
  with members:
    - "degree d chow parameters robustly determine degree d ptfs and algorithmic applications"
    - "algorithmic bayesian persuasion"
    - "public projects boolean functions and the borders of border s theorem"
    - "a robust khintchine inequality and algorithms for computing optimal constants in fourier analysis and high dimensional geometry"
    - "nearly optimal solutions for the chow parameters problem and low weight approximation of halfspaces"
    - "the complexity of power index comparison"
    - "complexity of comparison of influence of players in simple games"

Connected through hyperedge HE-3 (induced by source paper "approximating power by weights"):
  with members:
    - "representation compatible power indices"
    - "the average representation a cornucopia of power indices"
    - "the inverse problem for power distributions in committees"

Connected through hyperedge HE-4 (induced by source paper "importance in systems with interval decisions"):
  with members:
    - "measuring voting power in convex policy spaces"

Connected through hyperedge HE-5 (induced by source paper "the power of the largest player"):
  with members:
    - "the inverse problem for power distributions in committees"

Connected through hyperedge HE-6 (induced by source paper "shapley values in weighted voting games with random weights"):
  with members:
    - "cooperative games with overlapping coalitions"

Connected through hyperedge HE-7 (induced by source paper "mostly sunny a forecast of tomorrow s power index research"):
  with members:
    - "the prediction value"
    - "group activity selection problem"
    - "the inverse problem for power distributions in committees"

Connected through hyperedge HE-8 (induced by source paper "ready for the design of voting rules"):
  with members:
    - "the inverse problem for power distributions in committees"
    - "nearly optimal solutions for the chow parameters problem and low weight approximation of halfspaces"

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)