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: "visualizing and understanding curriculum learning for long short term memory networks"
  Abstract: Curriculum Learning emphasizes the order of training instances in a computational learning setup. The core hypothesis is that simpler instances should be learned early as building blocks to learn more complex ones. Despite its usefulness, it is still unknown how exactly the internal representation of models are affected by curriculum learning. In this paper, we study the effect of curriculum learning on Long Short-Term Memory (LSTM) networks, which have shown strong competency in many Natural Language Processing (NLP) problems. Our experiments on sentiment analysis task and a synthetic task similar to sequence prediction tasks in NLP show that curriculum learning has a positive effect on the LSTM's internal states by biasing the model towards building constructive representations i.e. the internal representation at the previous timesteps are used as building blocks for the final prediction. We also find that smaller models significantly improves when they are trained with curriculum learning. Lastly, we show that curriculum learning helps more when the amount of training data is limited.

Connected through hyperedge HE-1 (induced by source paper "data ordering patterns for neural machine translation an empirical study"):
  with members:
    - "competence based curriculum learning for neural machine translation"
    - "an empirical exploration of curriculum learning for neural machine translation"
    - "dynamic sentence sampling for efficient training of neural machine translation"
    - "automated curriculum learning for neural networks"
    - "curriculum learning and minibatch bucketing in neural machine translation"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "dynamic data selection for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "understanding and controlling memory in recurrent neural networks"):
  with members:
    - "sparse attentive backtracking temporal creditassignment through reminding"
    - "identifying and controlling important neurons in neural machine translation"
    - "the lambada dataset word prediction requiring a broad discourse context"
    - "control of memory active perception and action in minecraft"
    - "end to end memory networks"
    - "visualizing and understanding recurrent networks"
    - "adam a method for stochastic optimization"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"

Connected through hyperedge HE-3 (induced by source paper "curriculum learning in deep neural networks for financial forecasting"):
  with members:
    - "transfer learning for clinical time series analysis using recurrent neural networks"
    - "transfer learning for time series classification"
    - "automated curriculum learning for neural networks"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "wavenet a generative model for raw audio"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "an empirical exploration of curriculum learning for neural machine translation"):
  with members:
    - "scheduled multi task learning from syntax to translation"
    - "automated curriculum learning for neural networks"
    - "sockeye a toolkit for neural machine translation"
    - "a comprehensive study of batch construction strategies for recurrent neural networks in mxnet"
    - "dynamic data selection for neural machine translation"
    - "six challenges for neural machine translation"
    - "boosting neural machine translation"
    - "accelerating recurrent neural network training using sequence bucketing and multi gpu data parallelization"

Connected through hyperedge HE-5 (induced by source paper "curriculum learning of visual attribute clusters for multi task classification"):
  with members:
    - "automated curriculum learning for neural networks"
    - "adaptive svm learning with privileged information for domain adaptation"
    - "teacher student curriculum learning"
    - "curriculum learning for multi task classification of visual attributes"
    - "reverse curriculum generation for reinforcement learning"
    - "class rectification hard mining for imbalanced deep learning"
    - "multiple instance curriculum learning for weakly supervised object detection"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-6 (induced by source paper "convolutional experts constrained local model for facial landmark detection"):
  with members:
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "tensor fusion network for multimodal sentiment analysis"
    - "mosi multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos"
    - "cascaded continuous regression for real time incremental face tracking"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "facial feature point detection a comprehensive survey"

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)