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: "deep sequential neural network"
  Abstract: Neural Networks sequentially build high-level features through their successive layers. We propose here a new neural network model where each layer is associated with a set of candidate mappings. When an input is processed, at each layer, one mapping among these candidates is selected according to a sequential decision process. The resulting model is structured according to a DAG like architecture, so that a path from the root to a leaf node defines a sequence of transformations. Instead of considering global transformations, like in classical multilayer networks, this model allows us for learning a set of local transformations. It is thus able to process data with different characteristics through specific sequences of such local transformations, increasing the expression power of this model w.r.t a classical multilayered network. The learning algorithm is inspired from policy gradient techniques coming from the reinforcement learning domain and is used here instead of the classical back-propagation based gradient descent techniques. Experiments on different datasets show the relevance of this approach.

Connected through hyperedge HE-1 (induced by source paper "efficient content based sparse attention with routing transformers"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "adaptive attention span in transformers"
    - "generating wikipedia by summarizing long sequences"
    - "self attention with relative position representations"
    - "an analysis of neural language modeling at multiple scales"

Connected through hyperedge HE-2 (induced by source paper "large memory layers with product keys"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "transformer xl attentive language models beyond a fixed length context"
    - "cross lingual language model pretraining"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adaptive input representations for neural language modeling"

Connected through hyperedge HE-3 (induced by source paper "not all attention is needed gated attention network for sequence data"):
  with members:
    - "marginalized average attentional network for weakly supervised learning"
    - "star transformer"
    - "generating long sequences with sparse transformers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "gaternet dynamic filter selection in convolutional neural network via a dedicated global gating network"
    - "structured attention networks"
    - "learning structured text representations"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "urnet user resizable residual networks with conditional gating module"):
  with members:
    - "big little net an efficient multi scale feature representation for visual and speech recognition"
    - "efficient neural architecture search via parameter sharing"
    - "nisp pruning networks using neuron importance score propagation"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "adaptive neural networks for efficient inference"
    - "blockdrop dynamic inference paths in residual networks"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (induced by source paper "training decision trees as replacement for convolution layers"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "splinenets continuous neural decision graphs"
    - "style aggregated network for facial landmark detection"
    - "neural ordinary differential equations"
    - "conditional information gain networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep convolutional decision jungle for image classification"

Connected through hyperedge HE-6 (induced by source paper "maskconnect connectivity learning by gradient descent"):
  with members:
    - "multi fiber networks for video recognition"
    - "efficient neural architecture search via parameter sharing"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "large scale evolution of image classifiers"
    - "blockdrop dynamic inference paths in residual networks"
    - "hierarchical representations for efficient architecture search"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "pathnet evolution channels gradient descent in super neural networks"

Connected through hyperedge HE-7 (induced by source paper "dynamic representations toward efficient inference on deep neural networks by decision gates"):
  with members:
    - "label refinery improving imagenet classification through label progression"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "learning transferable architectures for scalable image recognition"
    - "adaptive neural networks for efficient inference"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "blockdrop dynamic inference paths in residual networks"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "mask r cnn"

Connected through hyperedge HE-8 (induced by source paper "you look twice gaternet for dynamic filter selection in cnns"):
  with members:
    - "learning sparse deep feedforward networks via tree skeleton expansion"
    - "discrete autoencoders for sequence models"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "learning transferable architectures for scalable image recognition"
    - "convolutional networks with adaptive inference graphs"
    - "improved regularization of convolutional neural networks with cutout"
    - "shake shake regularization"
    - "inception v4 inception resnet and the impact of residual connections on learning"

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.LG(Machine Learning)