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 "evidence aggregation for answer re ranking in open domain question answering"):
  Abstract: A popular recent approach to answering open-domain questions is to first search for question-related passages and then apply reading comprehension models to extract answers. Existing methods usually extract answers from single passages independently. But some questions require a combination of evidence from across different sources to answer correctly. In this paper, we propose two models which make use of multiple passages to generate their answers. Both use an answer-reranking approach which reorders the answer candidates generated by an existing state-of-the-art QA model. We propose two methods, namely, strength-based re-ranking and coverage-based re-ranking, to make use of the aggregated evidence from different passages to better determine the answer. Our models have achieved state-of-the-art results on three public open-domain QA datasets: Quasar-T, SearchQA and the open-domain version of TriviaQA, with about 8 percentage points of improvement over the former two datasets.

Includes members:
  Member "triviaqa a large scale distantly supervised challenge dataset for reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "searchqa a new q a dataset augmented with context from a search engine":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "r 3 reinforced reader ranker for open domain question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "k adapter infusing knowledge into pre trained models with adapters")
  Member "quasar datasets for question answering by search and reading":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "k adapter infusing knowledge into pre trained models with adapters")
  Member "ask the right questions active question reformulation with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "paraphrase generation with latent bag of words")
  Member "s net from answer extraction to answer generation for machine reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "text understanding with the attention sum reader network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "retrospective reader for machine reading comprehension")
  Member "recurrent neural network grammars":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")

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)