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: "using sentence level lstm language models for script inference"
  Abstract: There is a small but growing body of research on statistical scripts, models of event sequences that allow probabilistic inference of implicit events from documents. These systems operate on structured verb-argument events produced by an NLP pipeline. We compare these systems with recent Recurrent Neural Net models that directly operate on raw tokens to predict sentences, finding the latter to be roughly comparable to the former in terms of predicting missing events in documents.

Connected through hyperedge HE-1 (induced by source paper "read beyond the lines understanding the implied textual meaning via a skim and intensive reading model"):
  with members:
    - "detect camouflaged spam content via stoneskipping graph and text joint embedding for chinese character variation representation"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "simple recurrent units for highly parallelizable recurrence"
    - "attention is all you need"
    - "a large self annotated corpus for sarcasm"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "image to language understanding captioning approach"):
  with members:
    - "deep reinforcement learning based image captioning with embedding reward"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep residual learning for image recognition"
    - "learning like a child fast novel visual concept learning from sentence descriptions of images"
    - "rethinking the inception architecture for computer vision"
    - "from captions to visual concepts and back"

Connected through hyperedge HE-3 (induced by source paper "evaluation benchmarks and learning criteria for discourse aware sentence representations"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "enteval a holistic evaluation benchmark for entity representations"
    - "a unified linear time framework for sentence level discourse parsing"
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "evaluating discourse in structured text representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "linguistic knowledge and transferability of contextual representations"
    - "superglue a stickier benchmark for general purpose language understanding systems"

Connected through hyperedge HE-4 (induced by source paper "evaluation benchmarks and learning criteriafor discourse aware sentence representations"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "enteval a holistic evaluation benchmark for entity representations"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "linguistic knowledge and transferability of contextual representations"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"
    - "discourse aware neural rewards for coherent text generation"
    - "collecting diverse natural language inference problems for sentence representation evaluation"

Connected through hyperedge HE-5 (induced by source paper "everything happens for a reason discovering the purpose of actions in procedural text"):
  with members:
    - "tracking discrete and continuous entity state for process understanding"
    - "a structured learning approach to temporal relation extraction"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "allennlp a deep semantic natural language processing platform"
    - "reasoning about actions and state changes by injecting commonsense knowledge"
    - "what happened leveraging verbnet to predict the effects of actions in procedural text"
    - "simulating action dynamics with neural process networks"

Connected through hyperedge HE-6 (induced by source paper "harry potter and the action prediction challenge from natural language"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"
    - "contextualized word representations for reading comprehension"
    - "whodunnit crime drama as a case for natural language understanding"
    - "attention is all you need"
    - "the narrativeqa reading comprehension challenge"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-7 (induced by source paper "open domain event extraction using neural latent variable models"):
  with members:
    - "jointly multiple events extraction via attention based graph information aggregation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint extraction of events and entities within a document context"
    - "joint learning templates and slots for event schema induction"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "probabilistic frame induction"

Connected through hyperedge HE-8 (induced by source paper "query focused scenario construction"):
  with members:
    - "picking apart story salads"
    - "deep contextualized word representations"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "a simple neural network module for relational reasoning"
    - "on the effective use of pretraining for natural language inference"
    - "teaching machines to read and comprehend"
    - "adam a method for stochastic optimization"

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)