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 "meta answering for machine reading"):
  Abstract: We investigate a framework for machine reading, inspired by real world information-seeking problems, where a meta question answering system interacts with a black box environment. The environment encapsulates a competitive machine reader based on BERT, providing candidate answers to questions, and possibly some context. To validate the realism of our formulation, we ask humans to play the role of a meta-answerer. With just a small snippet of text around an answer, humans can outperform the machine reader, improving recall. Similarly, a simple machine meta-answerer outperforms the environment, improving both precision and recall on the Natural Questions dataset. The system relies on joint training of answer scoring and the selection of conditioning information.

Includes members:
  Member "latent retrieval for weakly supervised open domain question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "answering while summarizing multi task learning for multi hop qa with evidence extraction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical graph network for multi hop question answering")
  Member "synthetic qa corpora generation with roundtrip consistency":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "addressing semantic drift in question generation for semi supervised question answering")
  Member "revealing the importance of semantic retrieval for machine reading at scale":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "a geometric perspective on optimal representations for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepmdp learning continuous latent space models for representation learning")
  Member "a bert baseline for the natural questions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tydi qa a benchmark for information seeking question answering in typologically diverse languages")
  Member "probing neural network comprehension of natural language arguments":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reclor a reading comprehension dataset requiring logical reasoning")
  Member "episodic memory reader learning what to remember for question answering from streaming data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "interactive machine comprehension with information seeking agents")

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)