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: "a collaborative mechanism for crowdsourcing prediction problems"
  Abstract: Machine Learning competitions such as the Netflix Prize have proven reasonably successful as a method of "crowdsourcing" prediction tasks. But these competitions have a number of weaknesses, particularly in the incentive structure they create for the participants. We propose a new approach, called a Crowdsourced Learning Mechanism, in which participants collaboratively "learn" a hypothesis for a given prediction task. The approach draws heavily from the concept of a prediction market, where traders bet on the likelihood of a future event. In our framework, the mechanism continues to publish the current hypothesis, and participants can modify this hypothesis by wagering on an update. The critical incentive property is that a participant will profit an amount that scales according to how much her update improves performance on a released test set.

Connected through hyperedge HE-1 (induced by source paper "smooth markets a basic mechanism for organizing gradient based learners"):
  with members:
    - "logan latent optimisation for generative adversarial networks"
    - "finite regret and cycles with fixed step size via alternating gradient descent ascent"
    - "a closer look at the optimization landscapes of generative adversarial networks"
    - "differentiable game mechanics"
    - "the mechanics of n player differentiable games"
    - "global convergence to the equilibrium of gans using variational inequalities"
    - "the numerics of gans"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"

Connected through hyperedge HE-2 (induced by source paper "no regret and incentive compatible online learning"):
  with members:
    - "online prediction with selfish experts"
    - "multi period trading prediction markets with connections to machine learning"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-3 (induced by source paper "decentralized and collaborative ai on blockchain"):
  with members:
    - "trustless machine learning contracts evaluating and exchanging machine learning models on the ethereum blockchain"
    - "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"
    - "universal sentence encoder"
    - "casper the friendly finality gadget"

Connected through hyperedge HE-4 (induced by source paper "collaborative machine learning markets with data replication robust payments"):
  with members:
    - "towards efficient data valuation based on the shapley value"
    - "model based pricing for machine learning in a data marketplace"
    - "a marketplace for data an algorithmic solution"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-5 (induced by source paper "semantics representations and grammars for deep learning"):
  with members:
    - "deep online convex optimization by putting forecaster to sleep"
    - "blocks and fuel frameworks for deep learning"
    - "deep learning in neural networks"
    - "fast convergence of regularized learning in games"
    - "continuous control with deep reinforcement learning"
    - "gradient estimation using stochastic computation graphs"
    - "compatible value gradients for reinforcement learning of continuous deep policies"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-6 (induced by source paper "cortical prediction markets"):
  with members:
    - "a utility framework for bounded loss market makers"
    - "machine learning markets"
    - "a new understanding of prediction markets via no regret learning"

Connected through hyperedge HE-7 (induced by source paper "designing informative securities"):
  with members:
    - "representing aggregate belief through the competitive equilibrium of a securities market"
    - "compact securities markets for pareto optimal reallocation of risk"
    - "a new understanding of prediction markets via no regret learning"
    - "an optimization based framework for automated market making"
    - "complexity of combinatorial market makers"

Connected through hyperedge HE-8 (induced by source paper "isoelastic agents and wealth updates in machine learning markets"):
  with members:
    - "representing aggregate belief through the competitive equilibrium of a securities market"
    - "an empirical comparison of algorithms for aggregating expert predictions"
    - "learning performance of prediction markets with kelly bettors"
    - "machine learning markets"
    - "a new understanding of prediction markets via no regret 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.LG(Machine Learning)