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: "budget feasible mechanisms for experimental design"
  Abstract: We present a deterministic, polynomial time, budget feasible mechanism scheme, that is approximately truthful and yields a constant (≈ 12.98) factor approximation for the Experimental Design Problem (EDP). By applying previous work on budget feasible mechanisms with a submodular objective, one could only have derived either an exponential time deterministic mechanism or a randomized polynomial time mechanism. We also establish that no truthful, budget-feasible mechanism is possible within a factor 2 approximation, and show how to generalize our approach to a wide class of learning problems, beyond linear regression.

Connected through hyperedge HE-1 (induced by source paper "causal feature discovery through strategic modification"):
  with members:
    - "strategic adaptation to classifiers a causal perspective"
    - "the disparate effects of strategic manipulation"
    - "how do classifiers induce agents to invest effort strategically"
    - "the social cost of strategic classification"
    - "strategic classification from revealed preferences"
    - "truthful linear regression"
    - "buying private data without verification"
    - "linear regression as a non cooperative game"

Connected through hyperedge HE-2 (induced by source paper "towards data auctions with externalities"):
  with members:
    - "a marketplace for data an algorithmic solution"
    - "low cost learning via active data procurement"
    - "conducting truthful surveys cheaply"
    - "selling privacy at auction"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-3 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online"):
  with members:
    - "a nearly linear time algorithm for submodular maximization with a knapsack constraint"
    - "on budget feasible mechanism design for symmetric submodular objectives"
    - "simple and efficient budget feasible mechanisms for monotone submodular valuations"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "budget feasible mechanisms on matroids"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "coverage matching and beyond new results on budgeted mechanism design"
    - "submodular secretary problems cardinality matching and linear constraints"

Connected through hyperedge HE-4 (induced by source paper "optimal budget feasible mechanisms for additive valuations"):
  with members:
    - "on budget feasible mechanism design for symmetric submodular objectives"
    - "simple and efficient budget feasible mechanisms for monotone submodular valuations"
    - "budget feasible mechanisms on matroids"
    - "coverage matching and beyond new results on budgeted mechanism design"
    - "bayesian budget feasibility with posted pricing"
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "on the approximability of budget feasible mechanisms"
    - "frugal mechanism design via spectral techniques"

Connected through hyperedge HE-5 (induced by source paper "on budget feasible mechanism design for symmetric submodular objectives"):
  with members:
    - "simple and efficient budget feasible mechanisms for monotone submodular valuations"
    - "budget feasible mechanisms on matroids"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "coverage matching and beyond new results on budgeted mechanism design"
    - "bayesian budget feasibility with posted pricing"
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "incentives for privacy tradeoff in community sensing"
    - "budget feasible mechanism design from prior free to bayesian"

Connected through hyperedge HE-6 (induced by source paper "approximate supermodularity bounds for experimental design"):
  with members:
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "weakly submodular functions"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-7 (induced by source paper "submodular optimization under noise"):
  with members:
    - "when are welfare guarantees robust"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "tight bounds on low degree spectral concentration of submodular and xos functions"
    - "optimal bounds on approximation of submodular and xos functions by juntas"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning coverage functions and private release of marginals"
    - "the computational complexity of truthfulness in combinatorial auctions"
    - "symmetry and approximability of submodular maximization problems"

Connected through hyperedge HE-8 (induced by source paper "coverage matching and beyond new results on budgeted mechanism design"):
  with members:
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "budget feasible mechanism design from prior free to bayesian"
    - "on the approximability of budget feasible mechanisms"
    - "frugal mechanism design via spectral techniques"
    - "budget feasible mechanisms"

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)