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 "rc qed evaluating natural language derivations in multi hop reading comprehension"):
  Abstract: Recent studies revealed that reading comprehension (RC) systems learn to exploit annotation artifacts and other biases in current datasets. This allows systems to "cheat" by employing simple heuristics to answer questions, e.g. by relying on semantic type consistency. This means that current datasets are not well-suited to evaluate RC systems. To address this issue, we introduce RC-QED, a new RC task that requires giving not only the correct answer to a question, but also the reasoning employed for arriving at this answer. For this, we release a large benchmark dataset consisting of 12,000 answers and corresponding reasoning in form of natural language derivations. Experiments show that our benchmark is robust to simple heuristics and challenging for state-of-the-art neural path ranking approaches.

Includes members:
  Member "coarse grain fine grain coattention network for multi evidence question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "answering while summarizing multi task learning for multi hop qa with evidence extraction")
  Member "explain yourself leveraging language models for commonsense reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning from explanations with neural execution tree")
  Member "automated fact checking task formulations methods and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fact check worthiness detection as positive unlabelled learning")
  Member "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transformers as soft reasoners over language")
  Member "neural models for reasoning over multiple mentions using coreference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "multi hop inference for sentence level textgraphs how challenging is meaningfully combining information for science question answering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "qasc a dataset for question answering via sentence composition")
  Member "e snli natural language inference with natural language explanations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning from explanations with neural execution tree")
  Member "neural automated essay scoring and coherence modeling for adversarially crafted input":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi task learning for coherence modeling")

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)