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: "bidirectional attention flow for machine comprehension"
  Abstract: Machine comprehension (MC), answering a query about a given context paragraph, requires modeling complex interactions between the context and the query. Recently, attention mechanisms have been successfully extended to MC. Typically these methods use attention to focus on a small portion of the context and summarize it with a fixed-size vector, couple attentions temporally, and/or often form a uni-directional attention. In this paper we introduce the Bi-Directional Attention Flow (BIDAF) network, a multi-stage hierarchical process that represents the context at different levels of granularity and uses bi-directional attention flow mechanism to obtain a query-aware context representation without early summarization. Our experimental evaluations show that our model achieves the state-of-the-art results in Stanford Question Answering Dataset (SQuAD) and CNN/DailyMail cloze test.

Connected through hyperedge HE-1 (induced by source paper "description based text classification with reinforcement learning"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "docbert bert for document classification"
    - "question answering is a format when is it useful"
    - "entity relation extraction as multi turn question answering"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "coreference resolution as query based span prediction"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"

Connected through hyperedge HE-2 (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-3 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on the dimensionality of word embedding"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning transferable architectures for scalable image recognition"
    - "attention is all you need"
    - "multilingual hierarchical attention networks for document classification"
    - "a read write memory network for movie story understanding"

Connected through hyperedge HE-4 (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-5 (induced by source paper "a framework for evaluation of machine reading comprehension gold standards"):
  with members:
    - "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "quick and not so dirty unsupervised selection of justification sentences for multi hop question answering"
    - "compositional questions do not necessitate multi hop reasoning"
    - "question answering is a format when is it useful"
    - "understanding dataset design choices for multi hop reasoning"

Connected through hyperedge HE-6 (induced by source paper "beat the ai investigating adversarial human annotations for reading comprehension"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "assessing the benchmarking capacity of machine reading comprehension datasets"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "learning the difference that makes a difference with counterfactually augmented data"
    - "adversarial nli a new benchmark for natural language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "annotation artifacts in natural language inference data"

Connected through hyperedge HE-7 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games"):
  with members:
    - "graph constrained reinforcement learning for natural language action spaces"
    - "does order matter an empirical study on generating multiple keyphrases as a sequence"
    - "nail a general interactive fiction agent"
    - "algorithmic improvements for deep reinforcement learning applied to interactive fiction"
    - "interactive fiction games a colossal adventure"
    - "using local knowledge graph construction to scale seq2seq models to multi document inputs"
    - "on the variance of the adaptive learning rate and beyond"
    - "learning to speak and act in a fantasy text adventure game"

Connected through hyperedge HE-8 (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"

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)