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: "self assembling modular networks for interpretable multi hop reasoning"
  Abstract: Multi-hop QA requires a model to connect multiple pieces of evidence scattered in a long context to answer the question. The recently proposed HotpotQA (Yang et al., 2018) dataset is comprised of questions embodying four different multi-hop reasoning paradigms (two bridge entity setups, checking multiple properties, and comparing two entities), making it challenging for a single neural network to handle all four. In this work, we present an interpretable, controller-based Self-Assembling Neural Modular Network (Hu et al., 2017, 2018) for multi-hop reasoning, where we design four novel modules (Find, Relocate, Compare, NoOp) to perform unique types of language reasoning. Based on a question, our layout controller RNN dynamically infers a series of reasoning modules to construct the entire network. Empirically, we show that our dynamic, multi-hop modular network achieves significant improvements over the static, single-hop baseline (on both regular and adversarial evaluation). We further demonstrate the interpretability of our model via three analyses. First, the controller can softly decompose the multi-hop question into multiple single-hop sub-questions to promote compositional reasoning behavior of the main network. Second, the controller can predict layouts that conform to the layouts designed by human experts. Finally, the intermediate module can infer the entity that connects two distantly-located supporting facts by addressing the sub-question from the controller.

Connected through hyperedge HE-1 (induced by source paper "unsupervised question decomposition for question answering"):
  with members:
    - "break it down a question understanding benchmark"
    - "unsupervised question answering by cloze translation"
    - "compositional questions do not necessitate multi hop reasoning"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "revealing the importance of semantic retrieval for machine reading at scale"
    - "huggingface s transformers state of the art natural language processing"
    - "multi hop reading comprehension through question decomposition and rescoring"
    - "cross lingual language model pretraining"

Connected through hyperedge HE-2 (induced by source paper "dc bert decoupling question and document for efficient contextual encoding"):
  with members:
    - "q8bert quantized 8bit bert"
    - "latent retrieval for weakly supervised open domain question answering"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "bert rediscovers the classical nlp pipeline"
    - "retrieve read rerank towards end to end multi document reading comprehension"
    - "learning to retrieve reasoning paths over wikipedia graph for question answering"
    - "a discrete hard em approach for weakly supervised question answering"
    - "revealing the importance of semantic retrieval for machine reading at scale"

Connected through hyperedge HE-3 (induced by source paper "dynamic knowledge graph construction for zero shot commonsense question answering"):
  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"
    - "using local knowledge graph construction to scale seq2seq models to multi document inputs"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "kagnet knowledge aware graph networks for commonsense reasoning"
    - "ranking and selecting multi hop knowledge paths to better predict human needs"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "learning to write with cooperative discriminators"

Connected through hyperedge HE-4 (induced by source paper "neural module networks for reasoning over text"):
  with members:
    - "giving bert a calculator finding operations and arguments with reading comprehension"
    - "sg net syntax guided machine reading comprehension"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "a multi type multi span network for reading comprehension that requires discrete reasoning"
    - "neural compositional denotational semantics for question answering"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "allennlp a deep semantic natural language processing platform"

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)