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: "single stream parallelization of generalized lstm like rnns on a gpu"
  Abstract: Recurrent neural networks (RNNs) have shown outstanding performance on processing sequence data. However, they suffer from long training time, which demands parallel implementations of the training procedure. Parallelization of the training algorithms for RNNs are very challenging because internal recurrent paths form dependencies between two different time frames. In this paper, we first propose a generalized graph-based RNN structure that covers the most popular long short-term memory (LSTM) network. Then, we present a parallelization approach that automatically explores parallelisms of arbitrary RNNs by analyzing the graph structure. The experimental results show that the proposed approach shows great speed-up even with a single training stream, and further accelerates the training when combined with multiple parallel training streams.

Connected through hyperedge HE-1 (induced by source paper "pix2vox context aware 3d reconstruction from single and multi view images"):
  with members:
    - "dense 3d object reconstruction from a single depth view"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "realpoint3d point cloud generation from a single image with complex background"
    - "3d lmnet latent embedding matching for accurate and diverse 3d point cloud reconstruction from a single image"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "learning a multi view stereo machine"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"

Connected through hyperedge HE-2 (induced by source paper "elsa a throughput optimized design of an lstm accelerator for energy constrained devices"):
  with members:
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "c lstm enabling efficient lstm using structured compression techniques on fpgas"
    - "in datacenter performance analysis of a tensor processing unit"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "fpga based low power speech recognition with recurrent neural networks"
    - "unsupervised learning of video representations using lstms"
    - "parallel multi dimensional lstm with application to fast biomedical volumetric image segmentation"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-3 (induced by source paper "character level language modeling with hierarchical recurrent neural networks"):
  with members:
    - "gated word character recurrent language model"
    - "lightrnn memory and computation efficient recurrent neural networks"
    - "exploring the limits of language modeling"
    - "character level incremental speech recognition with recurrent neural networks"
    - "character based neural machine translation"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "character aware neural language models"
    - "blackout speeding up recurrent neural network language models with very large vocabularies"

Connected through hyperedge HE-4 (induced by source paper "fpga based low power speech recognition with recurrent neural networks"):
  with members:
    - "eie efficient inference engine on compressed deep neural network"
    - "character level incremental speech recognition with recurrent neural networks"
    - "fpga based implementation of deep neural networks using on chip memory only"
    - "fixed point performance analysis of recurrent neural networks"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "character level incremental speech recognition with recurrent neural networks"):
  with members:
    - "online sequence training of recurrent neural networks with connectionist temporal classification"
    - "end to end attention based large vocabulary speech recognition"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "first pass large vocabulary continuous speech recognition using bi directional recurrent dnns"
    - "deep speech scaling up end to end speech recognition"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-6 (induced by source paper "dynamic hand gesture recognition for wearable devices with low complexity recurrent neural networks"):
  with members:
    - "character level incremental speech recognition with recurrent neural networks"
    - "fixed point performance analysis of recurrent neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-7 (induced by source paper "online sequence training of recurrent neural networks with connectionist temporal classification"):
  with members:
    - "listen attend and spell"
    - "end to end attention based large vocabulary speech recognition"
    - "attention based models for speech recognition"
    - "deep speech scaling up end to end speech recognition"
    - "neural machine translation by jointly learning to align and translate"
    - "recurrent neural network regularization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "fixed point performance analysis of recurrent neural networks"):
  with members:
    - "show and tell a neural image caption generator"
    - "speech recognition with deep recurrent neural networks"
    - "adadelta an adaptive learning rate method"

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)