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: "linguistic knowledge as memory for recurrent neural networks"
  Abstract: Training recurrent neural networks to model long term dependencies is difficult. Hence, we propose to use external linguistic knowledge as an explicit signal to inform the model which memories it should utilize. Specifically, external knowledge is used to augment a sequence with typed edges between arbitrarily distant elements, and the resulting graph is decomposed into directed acyclic subgraphs. We introduce a model that encodes such graphs as explicit memory in recurrent neural networks, and use it to model coreference relations in text. We apply our model to several text comprehension tasks and achieve new state-of-the-art results on all considered benchmarks, including CNN, bAbi, and LAMBADA. On the bAbi QA tasks, our model solves 15 out of the 20 tasks with only 1000 training examples per task. Analysis of the learned representations further demonstrates the ability of our model to encode fine-grained entity information across a document.

Connected through hyperedge HE-1 (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-2 (induced by source paper "evidence sentence extraction for machine reading comprehension"):
  with members:
    - "dream a challenge dataset and models for dialogue based reading comprehension"
    - "training a ranking function for open domain question answering"
    - "ukp athene multi sentence textual entailment for claim verification"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "neural models for reasoning over multiple mentions using coreference"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "deep probabilistic logic a unifying framework for indirect supervision"

Connected through hyperedge HE-3 (induced by source paper "universal transformers"):
  with members:
    - "hyperbolic attention networks"
    - "weighted transformer network for machine translation"
    - "neural models for reasoning over multiple mentions using coreference"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "a structured self attentive sentence embedding"
    - "broad context language modeling as reading comprehension"

Connected through hyperedge HE-4 (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"
    - "reading wikipedia to answer open domain questions"
    - "proximal policy optimization algorithms"
    - "a deep reinforced model for abstractive summarization"

Connected through hyperedge HE-5 (induced by source paper "graph neural networks with generated parameters for relation extraction"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "improving entity linking by modeling latent relations between mentions"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "cross sentence n ary relation extraction with graph lstms"
    - "a simple neural network module for relational reasoning"
    - "scene graph generation by iterative message passing"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-6 (induced by source paper "dynamic integration of background knowledge in neural nlu systems"):
  with members:
    - "natural language inference over interaction space"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "learned in translation contextualized word vectors"
    - "semi supervised sequence tagging with bidirectional language models"
    - "making neural qa as simple as possible but not simpler"
    - "a neural language model for dynamically representing the meanings of unknown words and entities in a discourse"
    - "task oriented query reformulation with reinforcement learning"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-7 (induced by source paper "neural models for reasoning over multiple mentions using coreference"):
  with members:
    - "cross sentence n ary relation extraction with graph lstms"
    - "end to end neural coreference resolution"
    - "syntax aware lstm model for chinese semantic role labeling"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "dynamic entity representations in neural language models"
    - "constructing datasets for multi hop reading comprehension across documents"

Connected through hyperedge HE-8 (induced by source paper "entity tracking improves cloze style reading comprehension"):
  with members:
    - "neural models for reasoning over multiple mentions using coreference"
    - "attention over attention neural networks for reading comprehension"
    - "broad context language modeling as reading comprehension"
    - "text understanding with the attention sum reader network"
    - "the lambada dataset word prediction requiring a broad discourse context"
    - "learning global features for coreference resolution"
    - "tracking the world state with recurrent entity networks"
    - "who did what a large scale person centered cloze dataset"

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)