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: "describing multimedia content using attention based encoder decoder networks"
  Abstract: Whereas deep neural networks were first mostly used for classification tasks, they are rapidly expanding in the realm of structured output problems, where the observed target is composed of multiple random variables that have a rich joint distribution, given the input. In this paper we focus on the case where the input also has a rich structure and the input and output structures are somehow related. We describe systems that learn to attend to different places in the input, for each element of the output, for a variety of tasks: machine translation, image caption generation, video clip description, and speech recognition . All these systems are based on a shared set of building blocks: gated recurrent neural networks and convolutional neural networks , along with trained attention mechanisms. We report on experimental results with these systems, showing impressively good performance and the advantage of the attention mechanism.

Connected through hyperedge HE-1 (induced by source paper "infrared and 3d skeleton feature fusion for rgb d action recognition"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "how powerful are graph neural networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "intel realsense stereoscopic depth cameras"

Connected through hyperedge HE-2 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition"):
  with members:
    - "object part attention model for fine grained image classification"
    - "multi attention multi class constraint for fine grained image recognition"
    - "learning to navigate for fine grained classification"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "attend and rectify a gated attention mechanism for fine grained recovery"
    - "learn to pay attention"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "stroke constrained attention network for online handwritten mathematical expression recognition"):
  with members:
    - "cross modal self attention network for referring image segmentation"
    - "compact trilinear interaction for visual question answering"
    - "multimodal transformer for unaligned multimodal language sequences"
    - "attention aware compositional network for person re identification"
    - "a gru based encoder decoder approach with attention for online handwritten mathematical expression recognition"
    - "multimodal machine learning a survey and taxonomy"
    - "attention is all you need"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-4 (induced by source paper "see attend and brake an attention based saliency map prediction model for end to end driving"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "salient object detection a survey"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "predicting driver attention in critical situations"
    - "learning where to attend like a human driver"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-5 (induced by source paper "information maximizing visual question generation"):
  with members:
    - "neural baby talk"
    - "fast decoding in sequence models using discrete latent variables"
    - "visual curiosity learning to ask questions to learn visual recognition"
    - "learning to ask neural question generation for reading comprehension"
    - "inferring and executing programs for visual reasoning"
    - "visual question generation as dual task of visual question answering"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-6 (induced by source paper "non monotonic sequential text generation"):
  with members:
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "blockwise parallel decoding for deep autoregressive models"
    - "relational inductive biases deep learning and graph networks"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "texygen a benchmarking platform for text generation models"
    - "language gans falling short"
    - "the importance of generation order in language modeling"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-7 (induced by source paper "comic toward a compact image captioning model with attention"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "attention is all you need"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "attend to you personalized image captioning with context sequence memory networks"
    - "contrastive learning for image captioning"
    - "massive exploration of neural machine translation architectures"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-8 (induced by source paper "insertion based decoding with automatically inferred generation order"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "synchronous bidirectional neural machine translation"
    - "sequence generation from both sides to the middle"
    - "non monotonic sequential text generation"
    - "text infilling"
    - "self attention with relative position representations"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "a tree based decoder for neural machine translation"

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)