human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "sequential cooperative bayesian inference"):
  Abstract: Cooperation is often implicitly assumed when learning from other agents. Cooperation implies that the agent selecting the data, and the agent learning from the data, have the same goal, that the learner infer the intended hypothesis. Recent models in human and machine learning have demonstrated the possibility of cooperation. We seek foundational theoretical results for cooperative inference by Bayesian agents through sequential data. We develop novel approaches analyzing consistency, rate of convergence and stability of Sequential Cooperative Bayesian Inference (SCBI). Our analysis of the effectiveness, sample efficiency and robustness show that cooperation is not only possible in specific instances but theoretically well-founded in general. We discuss implications for human-human and human-machine cooperation.

Includes members:
  Member "pragmatic pedagogic value alignment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "a mathematical theory of cooperative communication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "badger learning to learn learning algorithms through multi agent communication")
  Member "generalizing the theory of cooperative inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "literal or pedagogic human analyzing human model misspecification in objective learning")
  Member "optimal cooperative inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "literal or pedagogic human analyzing human model misspecification in objective learning")
  Member "cooperative inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "infant directed speech is consistent with teaching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a mathematical theory of cooperative communication")
  Member "machine teaching for bayesian learners in the exponential family":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dataset distillation")

Question: Please tell me which class the source paper 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)