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 learning in business analytics and operations research models applications and managerial implications"
  Abstract: Business analytics refers to methods and practices that create value through data for individuals, firms, and organizations. This field is currently experiencing a radical shift due to the advent of deep learning: deep neural networks promise improvements in prediction performance as compared to models from traditional machine learning. However, our research into the existing body of literature reveals a scarcity of research works utilizing deep learning in our discipline. Accordingly, the objectives of this work are as follows: (1) we motivate why researchers and practitioners from business analytics should utilize deep neural networks and review potential use cases, necessary requirements, and benefits. (2) We investigate the added value to operations research in different case studies with real data from entrepreneurial undertakings. All such cases demonstrate a higher prediction performance in comparison to traditional machine learning and thus direct value gains. (3) We provide guidelines and implications for researchers, managers and practitioners in operations research who want to advance their capabilities for business analytics with regard to deep learning. (4) We finally discuss directions for future research in the field of business analytics.

Connected through hyperedge HE-1 (induced by source paper "putting question answering systems into practice transfer learning for efficient domain customization"):
  with members:
    - "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"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "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-2 (induced by source paper "sentiment analysis based on rhetorical structure theory learning deep neural networks from discourse trees"):
  with members:
    - "deep learning for affective computing text based emotion recognition in decision support"
    - "learning structured text representations"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "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-3 (induced by source paper "personalized purchase prediction of market baskets with wasserstein based sequence matching"):
  with members:
    - "recurrent neural networks with top k gains for session based recommendations"
    - "clustering for point pattern data"
    - "neural collaborative filtering"
    - "market basket prediction using user centric temporal annotated recurring sequences"
    - "session based recommendations with recurrent neural networks"
    - "distributed representations of words and phrases and their compositionality"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-4 (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"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "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-5 (induced by source paper "decision support with text based emotion recognition deep learning for affective computing"):
  with members:
    - "putting question answering systems into practice transfer learning for efficient domain customization"
    - "gradascent at emoint 2017 character and word level recurrent neural network models for tweet emotion intensity detection"
    - "predicting process behaviour using deep learning"
    - "fake news detection on social media a data mining perspective"
    - "how transferable are neural networks in nlp applications"
    - "detecting state of aggression in sentences using cnn"
    - "crowdsourcing a word emotion association lexicon"

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.LG(Machine Learning)