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 "interactive text ranking with bayesian optimisation a case study on community qa and summarisation"):
  Abstract: For many NLP applications, such as question answering and summarisation, the goal is to select the best solution from a large space of candidates to meet a particular user's needs. To address the lack of user-specific training data, we propose an interactive text ranking approach that actively selects pairs of candidates, from which the user selects the best. Unlike previous strategies, which attempt to learn a ranking across the whole candidate space, our method employs Bayesian optimisation to focus the user's labelling effort on high quality candidates and integrates prior knowledge in a Bayesian manner to cope better with small data scenarios. We apply our method to community question answering (cQA) and extractive summarisation, finding that it significantly outperforms existing interactive approaches. We also show that the ranking function learned by our method is an effective reward function for reinforcement learning, which improves the state of the art for interactive summarisation.

Includes members:
  Member "end to end robotic reinforcement learning without reward engineering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a framework for data driven robotics")
  Member "passage ranking with weak supervision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "revisit semantic representation and tree search for similar question retrieval")
  Member "preference based interactive multi document summarisation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fine tuning language models from human preferences")
  Member "april interactively learning to summarise by combining active preference learning and reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "selection via proxy efficient data selection for deep learning")
  Member "finding convincing arguments using scalable bayesian preference learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "preference based interactive multi document summarisation")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "improving a neural semantic parser by counterfactual learning from human bandit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "exploring neural models for parsing natural language into first order logic")

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)