human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "predicting confusion from eye tracking data with recurrent neural networks"):
  Abstract: Encouraged by the success of deep learning in a variety of domains, we investigate the suitability and effectiveness of Recurrent Neural Networks (RNNs) in a domain where deep learning has not yet been used; namely detecting confusion from eye-tracking data. Through experiments with a dataset of user interactions with ValueChart (an interactive visualization tool), we found that RNNs learn a feature representation from the raw data that allows for a more powerful classifier than previous methods that use engineered features. This is evidenced by the stronger performance of the RNN (0.74/0.71 sensitivity/specificity), as compared to a Random Forest classifier (0.51/0.70 sensitivity/specificity), when both are trained on an un-augmented dataset. However, using engineered features allows for simple data augmentation methods to be used. These same methods are not as effective at augmentation for the feature representation learned from the raw data, likely due to an inability to match the temporal dynamics of the data.

Includes members:
  Member "phased lstm accelerating recurrent network training for long or event based sequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "matrix lstm a differentiable recurrent surface for asynchronous event based data")
  Member "c rnn gan continuous recurrent neural networks with adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "music generation by deep learning challenges and directions")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "deep visual semantic alignments for generating image descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "learning phrase representations using rnn encoder decoder for statistical machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "representation learning a review and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "scikit learn machine learning in python":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scail classifier weights scaling for class incremental learning")
  Member "smote synthetic minority over sampling technique":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "refuge challenge a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs")

Question: Please tell me which class the source paper 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.CV(Computer Vision and Pattern Recognition)