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: "large scale qa srl parsing"
  Abstract: We present a new large-scale corpus of Question-Answer driven Semantic Role Labeling (QA-SRL) annotations, and the first high-quality QA-SRL parser. Our corpus, QA-SRL Bank 2.0, consists of over 250,000 question-answer pairs for over 64,000 sentences across 3 domains and was gathered with a new crowd-sourcing scheme that we show has high precision and good recall at modest cost. We also present neural models for two QA-SRL subtasks: detecting argument spans for a predicate and generating questions to label the semantic relationship. The best models achieve question accuracy of 82.6% and span-level accuracy of 77.6% (under human evaluation) on the full pipelined QA-SRL prediction task. They can also, as we show, be used to gather additional annotations at low cost.

Connected through hyperedge HE-1 (induced by source paper "question answering is a format when is it useful"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "machine reading comprehension a literature review"
    - "latent retrieval for weakly supervised open domain question answering"
    - "mathqa towards interpretable math word problem solving with operation based formalisms"
    - "learning and evaluating general linguistic intelligence"
    - "compositional questions do not necessitate multi hop reasoning"
    - "entity relation extraction as multi turn question answering"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"

Connected through hyperedge HE-2 (induced by source paper "learning and evaluating general linguistic intelligence"):
  with members:
    - "on first order meta learning algorithms"
    - "achieving human parity on automatic chinese to english news translation"
    - "the natural language decathlon multitask learning as question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "quac question answering in context"
    - "the description length of deep learning models"

Connected through hyperedge HE-3 (induced by source paper "understanding roles and entities datasets and models for natural language inference"):
  with members:
    - "paws paraphrase adversaries from word scrambling"
    - "multi task deep neural networks for natural language understanding"
    - "annotation artifacts in natural language inference data"
    - "transforming question answering datasets into natural language inference datasets"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "enhancing and combining sequential and tree lstm for natural language inference"

Connected through hyperedge HE-4 (induced by source paper "on the limits of learning to actively learn semantic representations"):
  with members:
    - "practical obstacles to deploying active learning"
    - "allennlp a deep semantic natural language processing platform"
    - "multitask parsing across semantic representations"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning active learning from data"
    - "learning algorithms for active learning"

Connected through hyperedge HE-5 (induced by source paper "crowdsourcing a high quality gold standard for qa srl"):
  with members:
    - "exploiting semantics in neural machine translation with graph convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "i know what you want semantic learning for text comprehension"):
  with members:
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "know what you don t know unanswerable questions for squad"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"
    - "one shot learning for question answering in gaokao history challenge"
    - "multi passage machine reading comprehension with cross passage answer verification"

Connected through hyperedge HE-7 (induced by source paper "jointly predicting predicates and arguments in neural semantic role labeling"):
  with members:
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "end to end neural coreference resolution"
    - "attention is all you need"
    - "a minimal span based neural constituency parser"
    - "deep semantic role labeling with self attention"
    - "end to end relation extraction using lstms on sequences and tree structures"

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)