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: "machine learning markets"
  Abstract: Prediction markets show considerable promise for developing flexible mechanisms for machine learning. Here, machine learning markets for multivariate systems are defined, and a utility-based framework is established for their analysis. This differs from the usual approach of defining static betting functions. It is shown that such markets can implement model combination methods used in machine learning, such as product of expert and mixture of expert approaches as equilibrium pricing models, by varying agent utility functions. They can also implement models composed of local potentials, and message passing methods. Prediction markets also allow for more flexible combinations, by combining multiple different utility functions. Conversely, the market mechanisms implement inference in the relevant probabilistic models. This means that market mechanism can be utilized for implementing parallelized model building and inference for probabilistic modelling.

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 "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-3 (induced by source paper "artificial prediction markets for online prediction of continuous variables a preliminary report"):
  with members:
    - "isoelastic agents and wealth updates in machine learning markets"
    - "a new understanding of prediction markets via no regret learning"

Connected through hyperedge HE-4 (induced by source paper "multi period trading prediction markets with connections to machine learning"):
  with members:
    - "toward a market model for bayesian inference"
    - "a utility framework for bounded loss market makers"
    - "isoelastic agents and wealth updates in machine learning markets"
    - "a new understanding of prediction markets via no regret learning"

Connected through hyperedge HE-5 (induced by source paper "cortical prediction markets"):
  with members:
    - "a utility framework for bounded loss market makers"
    - "a collaborative mechanism for crowdsourcing prediction problems"
    - "a new understanding of prediction markets via no regret learning"

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

Connected through hyperedge HE-7 (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"
    - "a collaborative mechanism for crowdsourcing prediction problems"
    - "a new understanding of prediction markets via no regret learning"

Connected through hyperedge HE-8 (induced by source paper "combinatorial modelling and learning with prediction markets"):
  with members:
    - "a utility framework for bounded loss market makers"
    - "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.AI(Artificial Intelligence)