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: "decision support from financial disclosures with deep neural networks and transfer learning"
  Abstract: Company disclosures greatly aid in the process of financial decision-making; therefore, they are consulted by financial investors and automated traders before exercising ownership in stocks. While humans are usually able to correctly interpret the content, the same is rarely true of computerized decision support systems, which struggle with the complexity and ambiguity of natural language. A possible remedy is represented by deep learning, which overcomes several shortcomings of traditional methods of text mining. For instance, recurrent neural networks, such as long short-term memories, employ hierarchical structures, together with a large number of hidden layers, to automatically extract features from ordered sequences of words and capture highly non-linear relationships such as context-dependent meanings. However, deep learning has only recently started to receive traction, possibly because its performance is largely untested. Hence, this paper studies the use of deep neural networks for financial decision support. We additionally experiment with transfer learning, in which we pre-train the network on a different corpus with a length of 139.1 million words. Our results reveal a higher directional accuracy as compared to traditional machine learning when predicting stock price movements in response to financial disclosures. Our work thereby helps to highlight the business value of deep learning and provides recommendations to practitioners and executives. We apply deep learning for sentiment analysis of financial news.This yields considerable improvements in forecasting the stock price movements.Additional gains stem from applying transfer learning.Long short-term memory performs overall best.Demonstrates the potential for future applications of deep learning in finance

Connected through hyperedge HE-1 (induced by source paper "a bert based sentiment analysis and key entity detection approach for online financial texts"):
  with members:
    - "few shot deep adversarial learning for video based person re identification"
    - "ernie enhanced language representation with informative entities"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "albert a lite bert for self supervised learning of language representations"
    - "cross entropy adversarial view adaptation for person re identification"
    - "pre training with whole word masking for chinese bert"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep learning for sentiment analysis a survey"

Connected through hyperedge HE-2 (induced by source paper "financial time series forecasting with deep learning a systematic literature review 2005 2019"):
  with members:
    - "financial trading model with stock bar chart image time series with deep convolutional neural networks"
    - "forecasting economics and financial time series arima vs lstm"
    - "sentiment predictability for stocks"
    - "lstm a search space odyssey"
    - "relational autoencoder for feature extraction"
    - "listening to chaotic whispers a deep learning framework for news oriented stock trend prediction"
    - "searching for activation functions"
    - "improving stock market prediction via heterogeneous information fusion"

Connected through hyperedge HE-3 (induced by source paper "deep transfer learning for source code modeling"):
  with members:
    - "code2vec learning distributed representations of code"
    - "an exploration of dropout with rnns for natural language inference"
    - "autofolding for source code summarization"
    - "attention is all you need"
    - "collective intelligence for smarter api recommendations in python"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "deep api learning"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-4 (induced by source paper "putting question answering systems into practice transfer learning for efficient domain customization"):
  with members:
    - "deep learning in business analytics and operations research models applications and managerial implications"
    - "deep learning for affective computing text based emotion recognition in decision support"
    - "adaptive document retrieval for deep question answering"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "reading wikipedia to answer open domain questions"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-5 (induced by source paper "finbert financial sentiment analysis with pre trained language models"):
  with members:
    - "how to fine tune bert for text classification"
    - "deep learning for sentiment analysis a survey"
    - "practical text classification with large pre trained language models"
    - "deep contextualized word representations"
    - "financial aspect based sentiment analysis using deep representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep learning a critical appraisal"

Connected through hyperedge HE-6 (induced by source paper "sentiment analysis based on rhetorical structure theory learning deep neural networks from discourse trees"):
  with members:
    - "deep learning in business analytics and operations research models applications and managerial implications"
    - "deep learning for affective computing text based emotion recognition in decision support"
    - "learning structured text representations"
    - "neural discourse structure for text categorization"
    - "representation learning for very short texts using weighted word embedding aggregation"
    - "better document level sentiment analysis from rst discourse parsing"
    - "improved semantic representations from tree structured long short term memory networks"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"

Connected through hyperedge HE-7 (induced by source paper "forecasting remaining useful life interpretable deep learning approach via variational bayesian inferences"):
  with members:
    - "verification for machine learning autonomy and neural networks survey"
    - "deep learning in business analytics and operations research models applications and managerial implications"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "a unified approach to interpreting model predictions"
    - "predicting process behaviour using deep learning"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-8 (induced by source paper "deep learning in business analytics and operations research models applications and managerial implications"):
  with members:
    - "putting question answering systems into practice transfer learning for efficient domain customization"
    - "sentiment analysis based on rhetorical structure theory learning deep neural networks from discourse trees"
    - "forecasting remaining useful life interpretable deep learning approach via variational bayesian inferences"
    - "similarity encoding for learning with dirty categorical variables"
    - "decision support with text based emotion recognition deep learning for affective computing"
    - "deep learning for affective computing text based emotion recognition in decision support"
    - "deep embedding forest forest based serving with deep embedding features"
    - "accelerating innovation through analogy mining"

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)