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: "memory efficient backpropagation through time"
  Abstract: We propose a novel approach to reduce memory consumption of the backpropagation through time (BPTT) algorithm when training recurrent neural networks (RNNs). Our approach uses dynamic programming to balance a trade-off between caching of intermediate results and recomputation. The algorithm is capable of tightly fitting within almost any user-set memory budget while finding an optimal execution policy minimizing the computational cost. Computational devices have limited memory capacity and maximizing a computational performance given a fixed memory budget is a practical use-case. We provide asymptotic computational upper bounds for various regimes. The algorithm is particularly effective for long sequences. For sequences of length 1000, our algorithm saves 95\% of memory usage while using only one third more time per iteration than the standard BPTT.

Connected through hyperedge HE-1 (induced by source paper "vq draw a sequential discrete vae"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "generating diverse high fidelity images with vq vae 2"
    - "group normalization"
    - "representation learning with contrastive predictive coding"
    - "progressive growing of gans for improved quality stability and variation"
    - "neural discrete representation learning"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"

Connected through hyperedge HE-2 (induced by source paper "supporting very large models using automatic dataflow graph partitioning"):
  with members:
    - "beyond data and model parallelism for deep neural networks"
    - "profile guided memory optimization for deep neural networks"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "exploring hidden dimensions in parallelizing convolutional neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "device placement optimization with reinforcement learning"
    - "a systematic approach to blocking convolutional neural networks"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"

Connected through hyperedge HE-3 (induced by source paper "continuous learning in a hierarchical multiscale neural network"):
  with members:
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "learning to generate reviews and discovering sentiment"
    - "regularizing and optimizing lstm language models"
    - "dynamic evaluation of neural sequence models"
    - "learning to learn by gradient descent by gradient descent"
    - "learning to optimize"
    - "recurrent memory network for language modeling"

Connected through hyperedge HE-4 (induced by source paper "dynamic control flow in large scale machine learning"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deep learning with dynamic computation graphs"
    - "real time machine learning the missing pieces"
    - "tricks from deep learning"
    - "adaptive computation time for recurrent neural networks"
    - "globally normalized transition based neural networks"
    - "training deep nets with sublinear memory cost"

Connected through hyperedge HE-5 (induced by source paper "meta learning a dynamical language model"):
  with members:
    - "fine tuned language models for text classification"
    - "learning to generate reviews and discovering sentiment"
    - "frustratingly short attention spans in neural language modeling"
    - "regularizing and optimizing lstm language models"
    - "dynamic evaluation of neural sequence models"
    - "learning to learn by gradient descent by gradient descent"
    - "learning to optimize"
    - "recurrent memory network for language modeling"

Connected through hyperedge HE-6 (induced by source paper "divide and conquer checkpointing for arbitrary programs with no user annotation"):
  with members:
    - "learning to learn by gradient descent by gradient descent"
    - "efficient implementation of a higher order language with built in ad"
    - "training deep nets with sublinear memory cost"
    - "binomial checkpointing for arbitrary programs with no user annotation"
    - "deep learning in neural networks"

Connected through hyperedge HE-7 (induced by source paper "extensions and limitations of the neural gpu"):
  with members:
    - "learning efficient algorithms with hierarchical attentive memory"
    - "adaptive computation time for recurrent neural networks"
    - "end to end memory networks"
    - "neural random access machines"
    - "learning simple algorithms from examples"
    - "learning to transduce with unbounded memory"
    - "grid long short term memory"
    - "inferring algorithmic patterns with stack augmented recurrent nets"

Connected through hyperedge HE-8 (induced by source paper "full resolution residual networks for semantic segmentation in street scenes"):
  with members:
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "voxresnet deep voxelwise residual networks for volumetric brain segmentation"
    - "stacked hourglass networks for human pose estimation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "laplacian reconstruction and refinement for semantic segmentation"
    - "bridging category level and instance level semantic image segmentation"
    - "identity mappings in deep residual networks"
    - "virtual worlds as proxy for multi object tracking analysis"

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.NE(Neural and Evolutionary Computing)