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: "repurposing entailment for multi hop question answering tasks"
  Abstract: Question Answering (QA) naturally reduces to an entailment problem, namely, verifying whether some text entails the answer to a question. However, for multi-hop QA tasks, which require reasoning with multiple sentences, it remains unclear how best to utilize entailment models pre-trained on large scale datasets such as SNLI, which are based on sentence pairs. We introduce Multee, a general architecture that can effectively use entailment models for multi-hop QA tasks. Multee uses (i) a local module that helps locate important sentences, thereby avoiding distracting information, and (ii) a global module that aggregates information by effectively incorporating importance weights. Importantly, we show that both modules can use entailment functions pre-trained on a large scale NLI datasets. We evaluate performance on MultiRC and OpenBookQA, two multihop QA datasets. When using an entailment function pre-trained on NLI datasets, Multee outperforms QA models trained only on the target QA datasets and the OpenAI transformer models. The code is available at this https URL.

Connected through hyperedge HE-1 (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"
    - "an interpretable reasoning network for multi relation question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "transfer learning for context aware question matching in information seeking conversations in e commerce"

Connected through hyperedge HE-2 (induced by source paper "multi hop question answering via reasoning chains"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "explore propose and assemble an interpretable model for multi hop reading comprehension"
    - "compositional questions do not necessitate multi hop reasoning"
    - "understanding dataset design choices for multi hop reasoning"
    - "evidence sentence extraction for machine reading comprehension"
    - "attention is not explanation"
    - "dynamically fused graph network for multi hop reasoning"
    - "multi hop reading comprehension through question decomposition and rescoring"

Connected through hyperedge HE-3 (induced by source paper "from f to a on the n y regents science exams an overview of the aristo project"):
  with members:
    - "mathqa towards interpretable math word problem solving with operation based formalisms"
    - "quartz an open domain dataset of qualitative relationship questions"
    - "roberta a robustly optimized bert pretraining approach"
    - "annotation artifacts in natural language inference data"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "hypothesis only baselines in natural language inference"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

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)