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 "grid long short term memory"):
  Abstract: This paper introduces Grid Long Short-Term Memory, a network of LSTM cells arranged in a multidimensional grid that can be applied to vectors, sequences or higher dimensional data such as images. The network differs from existing deep LSTM architectures in that the cells are connected between network layers as well as along the spatiotemporal dimensions of the data. The network provides a unified way of using LSTM for both deep and sequential computation. We apply the model to algorithmic tasks such as 15-digit integer addition and sequence memorization, where it is able to significantly outperform the standard LSTM. We then give results for two empirical tasks. We find that 2D Grid LSTM achieves 1.47 bits per character on the Wikipedia character prediction benchmark, which is state-of-the-art among neural approaches. In addition, we use the Grid LSTM to define a novel two-dimensional translation model, the Reencoder, and show that it outperforms a phrase-based reference system on a Chinese-to-English translation task.

Includes members:
  Member "lstm a search space odyssey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "gated feedback recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "accessing higher level representations in sequential transformers with feedback memory")
  Member "renet a recurrent neural network based alternative to convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rnnpool efficient non linear pooling for ram constrained inference")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "unifying visual semantic embeddings with multimodal neural language models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial ranking attack and defense")
  Member "convolutional kernel networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "why do deep residual networks generalize better than deep feedforward networks a neural tangent kernel perspective")
  Member "spatially sparse convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "occuseg occupancy aware 3d instance segmentation")
  Member "fractional max pooling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pointaugment an auto augmentation framework for point cloud classification")

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.NE(Neural and Evolutionary Computing)