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: "highway and residual networks learn unrolled iterative estimation"
  Abstract: The past year saw the introduction of new architectures such as Highway networks and Residual networks which, for the first time, enabled the training of feedforward networks with dozens to hundreds of layers using simple gradient descent. While depth of representation has been posited as a primary reason for their success, there are indications that these architectures defy a popular view of deep learning as a hierarchical computation of increasingly abstract features at each layer. #R##N#In this report, we argue that this view is incomplete and does not adequately explain several recent findings. We propose an alternative viewpoint based on unrolled iterative estimation -- a group of successive layers iteratively refine their estimates of the same features instead of computing an entirely new representation. We demonstrate that this viewpoint directly leads to the construction of Highway and Residual networks. Finally we provide preliminary experiments to discuss the similarities and differences between the two architectures.

Connected through hyperedge HE-1 (induced by source paper "toward fast and accurate human pose estimation via soft gated skip connections"):
  with members:
    - "anchor loss modulating loss scale based on prediction difficulty"
    - "deep high resolution representation learning for human pose estimation"
    - "quantized densely connected u nets for efficient landmark localization"
    - "cu net coupled u nets"
    - "multi scale structure aware network for human pose estimation"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "fractional skipping towards finer grained dynamic cnn inference"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "slimmable neural networks"
    - "hybrid pruning thinner sparse networks for fast inference on edge devices"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "haq hardware aware automated quantization with mixed precision"
    - "gaternet dynamic filter selection in convolutional neural network via a dedicated global gating network"
    - "skipnet learning dynamic routing in convolutional networks"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-3 (induced by source paper "a derivative free method for solving elliptic partial differential equations with deep neural networks"):
  with members:
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "hidden fluid mechanics a navier stokes informed deep learning framework for assimilating flow visualization data"
    - "learning across scales a multiscale method for convolution neural networks"
    - "searching for activation functions"
    - "swish a self gated activation function"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "e2 train training state of the art cnns with over 80 energy savings"):
  with members:
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "adascale towards real time video object detection using adaptive scaling"
    - "swalp stochastic weight averaging in low precision training"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "scalable methods for 8 bit training of neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-5 (induced by source paper "learning implicitly recurrent cnns through parameter sharing"):
  with members:
    - "convolutional neural networks with alternately updated clique"
    - "regularized evolution for image classifier architecture search"
    - "neural arithmetic logic units"
    - "snas stochastic neural architecture search"
    - "sparsely aggregated convolutional networks"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "making neural programming architectures generalize via recursion"

Connected through hyperedge HE-6 (induced by source paper "esfnet efficient network for building extraction from high resolution aerial images"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "exfuse enhancing feature fusion for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "interleaved group convolutions for deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "alert accurate anytime learning for energy and timeliness"):
  with members:
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "sparsely aggregated convolutional networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self attentive sequential recommendation"
    - "anytime stereo image depth estimation on mobile devices"
    - "in datacenter performance analysis of a tensor processing unit"
    - "learning transferable architectures for scalable image recognition"
    - "blockdrop dynamic inference paths in residual networks"

Connected through hyperedge HE-8 (induced by source paper "network implosion effective model compression for resnets via static layer pruning and retraining"):
  with members:
    - "learning strict identity mappings in deep residual networks"
    - "data driven sparse structure selection for deep neural networks"
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "convolutional sequence to sequence learning"
    - "adaptive learning rate via covariance matrix based preconditioning for deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "identity mappings in deep residual networks"

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)