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: "deep reinforcement learning for mention ranking coreference models"
  Abstract: Coreference resolution systems are typically trained with heuristic loss functions that require careful tuning. In this paper we instead apply reinforcement learning to directly optimize a neural mention-ranking model for coreference evaluation metrics. We experiment with two approaches: the REINFORCE policy gradient algorithm and a reward-rescaled max-margin objective. We find the latter to be more effective, resulting in significant improvements over the current state-of-the-art on the English and Chinese portions of the CoNLL 2012 Shared Task.

Connected through hyperedge HE-1 (induced by source paper "factor graph attention"):
  with members:
    - "a simple baseline for audio visual scene aware dialog"
    - "diverse and coherent paragraph generation from images"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "audio visual scene aware dialog avsd challenge at dstc7"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "two can play this game visual dialog with discriminative question generation and answering"

Connected through hyperedge HE-2 (induced by source paper "using local knowledge graph construction to scale seq2seq models to multi document inputs"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "multi news a large scale multi document summarization dataset and abstractive hierarchical model"
    - "transformer xl attentive language models beyond a fixed length context"
    - "lattice cnns for matching based chinese question answering"
    - "adaptive attention span in transformers"
    - "eli5 long form question answering"
    - "graph neural networks a review of methods and applications"
    - "building dynamic knowledge graphs from text using machine reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "learning to infer entities properties and their relations from clinical conversations"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "extracting symptoms and their status from clinical conversations"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "neural relation extraction within and across sentence boundaries"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "multi labeled relation extraction with attentive capsule network"
    - "multi task learning for sequence tagging an empirical study"
    - "a hierarchical framework for relation extraction with reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "winogrande an adversarial winograd schema challenge at scale"):
  with members:
    - "a surprisingly robust trick for the winograd schema challenge"
    - "solving hard coreference problems"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
    - "a hybrid neural network model for commonsense reasoning"
    - "probing neural network comprehension of natural language arguments"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"
    - "on adversarial removal of hypothesis only bias in natural language inference"

Connected through hyperedge HE-5 (induced by source paper "improving multi turn dialogue modelling with utterance rewriter"):
  with members:
    - "review driven answer generation for product related questions in e commerce"
    - "sentence rewriting for semantic parsing"
    - "cota improving the speed and accuracy of customer support through ranking and deep networks"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "an exploration of neural sequence to sequence architectures for automatic post editing"
    - "end to end neural coreference resolution"
    - "quickedit editing text translations by crossing words out"

Connected through hyperedge HE-6 (induced by source paper "toward gender inclusive coreference resolution"):
  with members:
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "assessing gender bias in machine translation a case study with google translate"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "pronoun translation in english french machine translation an analysis of error types"
    - "gender bias in coreference resolution"

Connected through hyperedge HE-7 (induced by source paper "multi hop reading comprehension via deep reinforcement learning based document traversal"):
  with members:
    - "variational reasoning for question answering with knowledge graph"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "making neural qa as simple as possible but not simpler"
    - "multi mention learning for reading comprehension with neural cascades"
    - "reinforced mnemonic reader for machine reading comprehension"
    - "linguistic knowledge as memory for recurrent neural networks"
    - "reading wikipedia to answer open domain questions"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-8 (induced by source paper "interconnected question generation with coreference alignment and conversation flow modeling"):
  with members:
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "generating distractors for reading comprehension questions from real examinations"
    - "coqa a conversational question answering challenge"
    - "difficulty controllable generation of reading comprehension questions"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "learning to ask neural question generation for reading comprehension"
    - "neural question generation from text a preliminary study"
    - "get to the point summarization with pointer generator networks"

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)