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: "who did what a large scale person centered cloze dataset"
  Abstract: We have constructed a new "Who-did-What" dataset of over 200,000 fill-in-the-gap (cloze) multiple choice reading comprehension problems constructed from the LDC English Gigaword newswire corpus. The WDW dataset has a variety of novel features. First, in contrast with the CNN and Daily Mail datasets (Hermann et al., 2015) we avoid using article summaries for question formation. Instead, each problem is formed from two independent articles --- an article given as the passage to be read and a separate article on the same events used to form the question. Second, we avoid anonymization --- each choice is a person named entity. Third, the problems have been filtered to remove a fraction that are easily solved by simple baselines, while remaining 84% solvable by humans. We report performance benchmarks of standard systems and propose the WDW dataset as a challenge task for the community.

Connected through hyperedge HE-1 (induced by source paper "learning cross context entity representations from text"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "end to end neural entity linking"
    - "ultra fine entity typing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "additive margin softmax for face verification"
    - "deeptype multilingual entity linking by neural type system evolution"

Connected through hyperedge HE-2 (induced by source paper "gennet reading comprehension with multiple choice questions using generation and selection model"):
  with members:
    - "eliminet a model for eliminating options for reading comprehension with multiple choice questions"
    - "convolutional spatial attention model for reading comprehension with multiple choice questions"
    - "option comparison network for multiple choice reading comprehension"
    - "dual co matching network for multi choice reading comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "a co matching model for multi choice reading comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "s net from answer extraction to answer generation for machine reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "tvqa spatio temporal grounding for video question answering"):
  with members:
    - "neural baby talk"
    - "know what you don t know unanswerable questions for squad"
    - "mattnet modular attention network for referring expression comprehension"
    - "grounded video description"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tvqa localized compositional video question answering"
    - "bsn boundary sensitive network for temporal action proposal generation"

Connected through hyperedge HE-4 (induced by source paper "discourse aware semantic self attention for narrative reading comprehension"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "transformer xl attentive language models beyond a fixed length context"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "self attention with relative position representations"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-5 (induced by source paper "multi relational question answering from narratives machine reading and reasoning in simulated worlds"):
  with members:
    - "a knowledge grounded neural conversation model"
    - "race large scale reading comprehension dataset from examinations"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "the narrativeqa reading comprehension challenge"
    - "answering complex questions using open information extraction"
    - "newsqa a machine comprehension dataset"
    - "a thorough examination of the cnn daily mail reading comprehension task"

Connected through hyperedge HE-6 (induced by source paper "simple but effective techniques to reduce biases"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "don t take the premise for granted mitigating artifacts in natural language inference"
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "rubi reducing unimodal biases in visual question answering"
    - "unlearn dataset bias in natural language inference by fitting the residual"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
    - "probing neural network comprehension of natural language arguments"
    - "on adversarial removal of hypothesis only bias in natural language inference"

Connected through hyperedge HE-7 (induced by source paper "towards debiasing fact verification models"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "identifying and correcting label bias in machine learning"
    - "probing neural network comprehension of natural language arguments"
    - "combining fact extraction and verification with neural semantic matching networks"
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "know what you don t know unanswerable questions for squad"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-8 (induced by source paper "question answering for privacy policies combining computational and legal perspectives"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"
    - "polisis automated analysis and presentation of privacy policies using deep learning"
    - "quac question answering in context"
    - "stress test evaluation for natural language inference"

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)