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: "generalizing the theory of cooperative inference"
  Abstract: Cooperation information sharing is important to theories of human learning and has potential implications for machine learning. Prior work derived conditions for achieving optimal Cooperative Inference given strong, relatively restrictive assumptions. We relax these assumptions by demonstrating convergence for any discrete joint distribution, robustness through equivalence classes and stability under perturbation, and effectiveness by deriving bounds from structural properties of the original joint distribution. We provide geometric interpretations, connections to and implications for optimal transport, and connections to importance sampling, and conclude by outlining open questions and challenges to realizing the promise of Cooperative Inference.

Connected through hyperedge HE-1 (induced by source paper "sequential cooperative bayesian inference"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "a mathematical theory of cooperative communication"
    - "optimal cooperative inference"
    - "cooperative inverse reinforcement learning"
    - "infant directed speech is consistent with teaching"
    - "machine teaching for bayesian learners in the exponential family"

Connected through hyperedge HE-2 (induced by source paper "literal or pedagogic human analyzing human model misspecification in objective learning"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "an efficient generalized bellman update for cooperative inverse reinforcement learning"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "optimal cooperative inference"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "continuous inverse optimal control with locally optimal examples"

Connected through hyperedge HE-3 (induced by source paper "a mathematical theory of cooperative communication"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "optimal cooperative inference"
    - "toward a general scaleable framework for bayesian teaching with applications to topic models"
    - "infant directed speech is consistent with teaching"
    - "optimal transport for domain adaptation"

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)