human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "a study of the tasks and models in machine reading comprehension"):
  with members:
    - "a deep cascade model for multi document reading comprehension"
    - "multi style generative reading comprehension"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "attention guided answer distillation for machine reading comprehension"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-2 (induced by source paper "k adapter infusing knowledge into pre trained models with adapters"):
  with members:
    - "pretrained encyclopedia weakly supervised knowledge pretrained language model"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "kepler a unified model for knowledge embedding and pre trained language representation"
    - "language models as knowledge bases"
    - "parameter efficient transfer learning for nlp"
    - "bert is not a knowledge base yet factual knowledge vs name based reasoning in unsupervised qa"
    - "informing unsupervised pretraining with external linguistic knowledge"
    - "matching the blanks distributional similarity for relation learning"

Connected through hyperedge HE-3 (induced by source paper "quick and not so dirty unsupervised selection of justification sentences for multi hop question answering"):
  with members:
    - "improving question answering with external knowledge"
    - "understanding dataset design choices for multi hop reasoning"
    - "evidence sentence extraction for machine reading comprehension"
    - "question answering as global reasoning over semantic abstractions"
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "repurposing entailment for multi hop question answering tasks"
    - "an interpretable reasoning network for multi relation question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"

Connected through hyperedge HE-4 (induced by source paper "coarse grain fine grain coattention network for multi evidence question answering"):
  with members:
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "efficient and robust question answering from minimal context over documents"
    - "neural models for reasoning over multiple mentions using coreference"
    - "global locally self attentive dialogue state tracker"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "coarse to fine decoding for neural semantic parsing"

Connected through hyperedge HE-5 (induced by source paper "jec qa a legal domain question answering dataset"):
  with members:
    - "convolutional spatial attention model for reading comprehension with multiple choice questions"
    - "interpretable charge predictions for criminal cases learning to generate court views from fact descriptions"
    - "cail2018 a large scale legal dataset for judgment prediction"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "know what you don t know unanswerable questions for squad"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "multi step retriever reader interaction for scalable open domain question answering"):
  with members:
    - "training a ranking function for open domain question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "efficient and robust question answering from minimal context over documents"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "emergent communication through negotiation"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "fast k nearest neighbour search via prioritized dci"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

Connected through hyperedge HE-7 (induced by source paper "answering while summarizing multi task learning for multi hop qa with evidence extraction"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "team papelo transformer networks at fever"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "evidence sentence extraction for machine reading comprehension"
    - "dynamically fused graph network for multi hop reasoning"
    - "combining fact extraction and verification with neural semantic matching networks"
    - "the fact extraction and verification fever shared task"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-8 (induced by source paper "learning to retrieve reasoning paths over wikipedia graph for question answering"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "passage re ranking with bert"
    - "do multi hop readers dream of reasoning chains"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "compositional questions do not necessitate multi hop reasoning"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "multi hop paragraph retrieval for open domain question answering"
    - "retrieve read rerank towards end to end multi document reading comprehension"

Question: Please tell me which class the center node 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)