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: "gate variants of gated recurrent unit gru neural networks"
  Abstract: The paper evaluates three variants of the Gated Recurrent Unit (GRU) in recurrent neural networks (RNN) by reducing parameters in the update and reset gates. We evaluate the three variant GRU models on MNIST and IMDB datasets and show that these GRU-RNN variant models perform as well as the original GRU RNN model while reducing the computational expense.

Connected through hyperedge HE-1 (induced by source paper "spatio temporal pyramid graph convolutions for human action recognition and postural assessment"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "skeleton based action recognition of people handling objects"
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "toward ergonomic risk prediction via segmentation of indoor object manipulation actions using spatiotemporal convolutional networks"
    - "unsupervised discovery of parts structure and dynamics"
    - "open set domain adaptation for image and action recognition"
    - "a comprehensive survey on graph neural networks"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-2 (induced by source paper "effective transfer learning for hyperspectral image classification with deep convolutional neural networks"):
  with members:
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "how transferable are features in deep neural networks"
    - "network in network"

Connected through hyperedge HE-3 (induced by source paper "performance of three slim variants of the long short term memory lstm layer"):
  with members:
    - "slim lstms"
    - "lstm a search space odyssey"
    - "simplified long short term memory recurrent neural networks part iii"
    - "simplified gating in long short term memory lstm recurrent neural networks"
    - "simplified minimal gated unit variations for recurrent neural networks"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-4 (induced by source paper "representative datasets the perceptron case"):
  with members:
    - "pull out all the stops textual analysis via punctuation sequences"
    - "deepprof performance analysis for deep learning applications via mining gpu execution patterns"
    - "adversarial examples attacks and defenses for deep learning"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-5 (induced by source paper "estimating forces of robotic pouring using a lstm rnn"):
  with members:
    - "a dataset of daily interactive manipulation"
    - "lstm a search space odyssey"
    - "functional object oriented network for manipulation learning"

Connected through hyperedge HE-6 (induced by source paper "gear training a new way to implement high performance model parallel training"):
  with members:
    - "ampnet asynchronous model parallel training for dynamic neural networks"
    - "stochastic gradient descent on highly parallel architectures"
    - "bigdl a distributed deep learning framework for big data"
    - "revisiting distributed synchronous sgd"
    - "wide deep learning for recommender systems"
    - "distributed tensorflow with mpi"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "image recognition using scale recurrent neural networks"):
  with members:
    - "texture networks feed forward synthesis of textures and stylized images"
    - "feature pyramid networks for object detection"
    - "hierarchical multiscale recurrent neural networks"
    - "deep residual learning for image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "imagenet large scale visual recognition challenge"

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)