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: "a discrete hard em approach for weakly supervised question answering"
  Abstract: Many question answering (QA) tasks only provide weak supervision for how the answer should be computed. For example, TriviaQA answers are entities that can be mentioned multiple times in supporting documents, while DROP answers can be computed by deriving many different equations from numbers in the reference text. In this paper, we show it is possible to convert such tasks into discrete latent variable learning problems with a precomputed, task-specific set of possible "solutions" (e.g. different mentions or equations) that contains one correct option. We then develop a hard EM learning scheme that computes gradients relative to the most likely solution at each update. Despite its simplicity, we show that this approach significantly outperforms previous methods on six QA tasks, including absolute gains of 2--10%, and achieves the state-of-the-art on five of them. Using hard updates instead of maximizing marginal likelihood is key to these results as it encourages the model to find the one correct answer, which we show through detailed qualitative analysis.

Connected through hyperedge HE-1 (induced by source paper "differentiable reasoning over a virtual knowledge base"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "bag bi directional attention entity graph convolutional network for multi hop reasoning question answering"
    - "explore propose and assemble an interpretable model for multi hop reading comprehension"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "neural query language a knowledge base query language for tensorflow"
    - "multi hop paragraph retrieval for open domain question answering"
    - "leveraging knowledge bases in lstms for improving machine reading"

Connected through hyperedge HE-2 (induced by source paper "realm retrieval augmented language model pre training"):
  with members:
    - "how much knowledge can you pack into the parameters of a language model"
    - "generalization through memorization nearest neighbor language models"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "language models as knowledge bases"
    - "latent retrieval for weakly supervised open domain question answering"
    - "knowledge guided text retrieval and reading for open domain question answering"
    - "spanbert improving pre training by representing and predicting spans"
    - "learning to retrieve reasoning paths over wikipedia graph for question answering"

Connected through hyperedge HE-3 (induced by source paper "how much knowledge can you pack into the parameters of a language model"):
  with members:
    - "realm retrieval augmented language model pre training"
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "language models as knowledge bases"
    - "latent retrieval for weakly supervised open domain question answering"
    - "knowledge guided text retrieval and reading for open domain question answering"
    - "how can we know what language models know"
    - "frustratingly easy natural question answering"

Connected through hyperedge HE-4 (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"
    - "self assembling modular networks for interpretable multi hop reasoning"
    - "revealing the importance of semantic retrieval for machine reading at scale"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (induced by source paper "knowledge guided text retrieval and reading for open domain question answering"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "compositional questions do not necessitate multi hop reasoning"
    - "multi passage bert a globally normalized bert model for open domain question answering"
    - "a bert baseline for the natural questions"
    - "multi step entity centric information retrieval for multi hop question answering"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"

Connected through hyperedge HE-7 (induced by source paper "contextualized sparse representation with rectified n gram attention for open domain question answering"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "multi passage bert a globally normalized bert model for open domain question answering"
    - "rankqa neural question answering with answer re ranking"
    - "ranking paragraphs for improving answer recall in open domain question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "attention is all you need"
    - "zero shot relation extraction via 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)