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 "neural conversational qa learning to reason v s exploiting patterns"):
  Abstract: In this paper we work on the recently introduced ShARC task - a challenging form of conversational QA that requires reasoning over rules expressed in natural language. Attuned to the risk of superficial patterns in data being exploited by neural models to do well on benchmark tasks (Niven and Kao 2019), we conduct a series of probing experiments and demonstrate how current state-of-the-art models rely heavily on such patterns. To prevent models from learning based on the superficial clues, we modify the dataset by automatically generating new instances reducing the occurrences of those patterns. We also present a simple yet effective model that learns embedding representations to incorporate dialog history along with the previous answers to follow-up questions. We find that our model outperforms existing methods on all metrics, and the results show that the proposed model is more robust in dealing with spurious patterns and learns to reason meaningfully.

Includes members:
  Member "bert with history answer embedding for conversational question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "curriculum learning strategies for ir an empirical study on conversation response ranking")
  Member "e3 entailment driven extracting and editing for conversational machine reading":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "crown conversational passage ranking by reasoning over word networks")
  Member "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial filters of dataset biases")
  Member "probing neural network comprehension of natural language arguments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reclor a reading comprehension dataset requiring logical reasoning")
  Member "can a suit of armor conduct electricity a new dataset for open book question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "interpretation of natural language rules in conversational machine reading":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "hotpotqa a dataset for diverse explainable multi hop question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "coqa a conversational question answering challenge":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on contextual embeddings")

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)