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: "residual connections encourage iterative inference"
  Abstract: Residual networks (Resnets) have become a prominent architecture in deep learning. However, a comprehensive understanding of Resnets is still a topic of ongoing research. #R##N#A recent view argues that Resnets perform iterative refinement of features. We attempt to further expose properties of this aspect. To this end, we study Resnets both analytically and empirically. We formalize the notion of iterative refinement in Resnets by showing that residual connections naturally encourage features of residual blocks to move along the negative gradient of loss as we go from one block to the next. In addition, our empirical analysis suggests that Resnets are able to perform both representation learning and iterative refinement. In general, a Resnet block tends to concentrate representation learning behavior in the first few layers while higher layers perform iterative refinement of features. Finally we observe that sharing residual layers naively leads to representation explosion and counterintuitively, overfitting, and we show that simple existing strategies can help alleviating this problem.

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 "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-3 (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-4 (induced by source paper "an investigation of model free planning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "relational deep reinforcement learning"
    - "learning to search with mctsnets"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "lipschitz continuity in model based reinforcement learning"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "distributed prioritized experience replay"

Connected through hyperedge HE-5 (induced by source paper "brain like object recognition with high performing shallow recurrent anns"):
  with members:
    - "do better imagenet models transfer better"
    - "exploring the limits of weakly supervised pretraining"
    - "iamnn iterative and adaptive mobile neural network for efficient image classification"
    - "progressive neural architecture search"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-6 (induced by source paper "deep learning via dynamical systems an approximation perspective"):
  with members:
    - "nonlinear approximation and deep relu networks"
    - "nonlinear approximation via compositions"
    - "barron spaces and the compositional function spaces for neural network models"
    - "understanding and improving transformer from a multi particle dynamic system point of view"
    - "a priori estimates of the population risk for residual networks"
    - "approximation capabilities of neural ordinary differential equations"
    - "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"
    - "dynamically unfolding recurrent restorer a moving endpoint control method for image restoration"

Connected through hyperedge HE-7 (induced by source paper "identity connections in residual nets improve noise stability"):
  with members:
    - "norm preservation why residual networks can become extremely deep"
    - "skip connections eliminate singularities"
    - "the loss surface of residual networks ensembles and the role of batch normalization"
    - "multi residual networks improving the speed and accuracy of residual networks"
    - "deep networks with stochastic depth"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "highway and residual networks learn unrolled iterative estimation"
    - "identity matters in deep learning"

Connected through hyperedge HE-8 (induced by source paper "convolutional neural networks with alternately updated clique"):
  with members:
    - "squeeze and excitation networks"
    - "dual path networks"
    - "residual attention network for image classification"
    - "dual attention networks for multimodal reasoning and matching"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"
    - "densely connected convolutional networks"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"

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.CV(Computer Vision and Pattern Recognition)