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 "self regulated interactive sequence to sequence learning"):
  Abstract: Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning. We show how self-regulation strategies that decide when to ask for which kind of feedback from a teacher (or from oneself) can be cast as a learning-to-learn problem leading to improved cost-aware sequence-to-sequence learning. In experiments on interactive neural machine translation, we find that the self-regulator discovers an $\epsilon$-greedy strategy for the optimal cost-quality trade-off by mixing different feedback types including corrections, error markups, and self-supervision. Furthermore, we demonstrate its robustness under domain shift and identify it as a promising alternative to active learning.

Includes members:
  Member "reinforcement learning based curriculum optimization for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimizing data usage via differentiable rewards")
  Member "joey nmt a minimalist nmt toolkit for novices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evaluating sequence to sequence learning models for if then program synthesis")
  Member "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fine tuning language models from human preferences")
  Member "learning to teach with dynamic loss functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online meta critic learning for off policy actor critic methods")
  Member "a reinforcement learning approach to interactive predictive neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "text as environment a deep reinforcement learning text readability assessment model")
  Member "active learning for interactive neural machine translation of data streams":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "selection via proxy efficient data selection for deep learning")
  Member "learning from chunk based feedback in neural machine translation":
    also connected through other hyperedges:
      (none)
  Member "a call for clarity in reporting bleu scores":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multilingual denoising pre training for neural machine translation")

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.CL(Computation and Language)