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: "optimal cooperative inference"
  Abstract: Cooperative transmission of data fosters rapid accumulation of knowledge by efficiently combining experiences across learners. Although well studied in human learning and increasingly in machine learning, we lack formal frameworks through which we may reason about the benefits and limitations of cooperative inference. We present such a framework. We introduce novel indices for measuring the effectiveness of probabilistic and cooperative information transmission. We relate our indices to the well-known Teaching Dimension in deterministic settings. We prove conditions under which optimal cooperative inference can be achieved, including a representation theorem that constrains the form of inductive biases for learners optimized for cooperative inference. We conclude by demonstrating how these principles may inform the design of machine learning algorithms and discuss implications for human and machine learning.

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"
    - "generalizing the theory of 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"
    - "generalizing the theory of 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"
    - "generalizing the theory of 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"

Connected through hyperedge HE-4 (induced by source paper "generalizing the theory of cooperative inference"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "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)