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: "eliminet a model for eliminating options for reading comprehension with multiple choice questions"
  Abstract: The task of Reading Comprehension with Multiple Choice Questions, requires a human (or machine) to read a given passage, question pair and select one of the n given options. The current state of the art model for this task first computes a question-aware representation for the passage and then selects the option which has the maximum similarity with this representation. However, when humans perform this task they do not just focus on option selection but use a combination of elimination and selection. Specifically, a human would first try to eliminate the most irrelevant option and then read the passage again in the light of this new information (and perhaps ignore portions corresponding to the eliminated option). This process could be repeated multiple times till the reader is finally ready to select the correct option. We propose ElimiNet, a neural network-based model which tries to mimic this process. Specifically, it has gates which decide whether an option can be eliminated given the passage, question pair and if so it tries to make the passage representation orthogonal to this eliminated option (akin to ignoring portions of the passage corresponding to the eliminated option). The model makes multiple rounds of partial elimination to refine the passage representation and finally uses a selection module to pick the best option. We evaluate our model on the recently released large scale RACE dataset and show that it outperforms the current state of the art model on 7 out of the $13$ question types in this dataset. Further, we show that taking an ensemble of our elimination-selection based method with a selection based method gives us an improvement of 3.1% over the best-reported performance on this dataset.

Connected through hyperedge HE-1 (induced by source paper "gennet reading comprehension with multiple choice questions using generation and selection model"):
  with members:
    - "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"
    - "a thorough examination of the cnn daily mail reading comprehension task"

Connected through hyperedge HE-2 (induced by source paper "convolutional spatial attention model for reading comprehension with multiple choice questions"):
  with members:
    - "deep contextualized word representations"
    - "hfl rc system at semeval 2018 task 11 hybrid multi aspects model for commonsense reading comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "reinforced mnemonic reader for machine comprehension"
    - "race large scale reading comprehension dataset from examinations"
    - "attention over attention neural networks for reading comprehension"
    - "fusionnet fusing via fully aware attention with application to machine comprehension"
    - "dynamic fusion networks for machine reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "option comparison network for multiple choice reading comprehension"):
  with members:
    - "deep contextualized word representations"
    - "multi range reasoning for machine comprehension"
    - "improving machine reading comprehension with general reading strategies"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "race large scale reading comprehension dataset from examinations"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "attention based convolutional neural network for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-4 (induced by source paper "improving machine reading comprehension with general reading strategies"):
  with members:
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "generating wikipedia by summarizing long sequences"
    - "what makes reading comprehension questions easier"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "coqa a conversational question answering challenge"
    - "multi range reasoning for machine comprehension"
    - "hfl rc system at semeval 2018 task 11 hybrid multi aspects model for commonsense reading comprehension"
    - "qanet combining local convolution with global self attention for 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)