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: "the loss surface of residual networks ensembles and the role of batch normalization"
  Abstract: Deep Residual Networks present a premium in performance in comparison to conventional#N#networks of the same depth and are trainable at extreme depths. It has#N#recently been shown that Residual Networks behave like ensembles of relatively#N#shallow networks. We show that these ensemble are dynamic: while initially#N#the virtual ensemble is mostly at depths lower than half the network’s depth, as#N#training progresses, it becomes deeper and deeper. The main mechanism that controls#N#the dynamic ensemble behavior is the scaling introduced, e.g., by the Batch#N#Normalization technique. We explain this behavior and demonstrate the driving#N#force behind it. As a main tool in our analysis, we employ generalized spin glass#N#models, which we also use in order to study the number of critical points in the#N#optimization of Residual Networks.

Connected through hyperedge HE-1 (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"
    - "residual connections encourage iterative inference"
    - "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-2 (induced by source paper "approximate fisher information matrix to characterise the training of deep neural networks"):
  with members:
    - "deep networks with stochastic depth"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "densely connected convolutional networks"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "skip connections as effective symmetry breaking"):
  with members:
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "aggregated residual transformations for deep neural networks"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "identity matters in deep learning"
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"
    - "reducing overfitting in deep networks by decorrelating representations"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "residual connections encourage iterative inference"):
  with members:
    - "learning deep resnet blocks sequentially using boosting theory"
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "identity mappings in deep residual networks"
    - "recurrent orthogonal networks and long memory tasks"
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"
    - "recurrent batch normalization"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "wider or deeper revisiting the resnet model for visual recognition"):
  with members:
    - "megdet a large mini batch object detector"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "semantic understanding of scenes through the ade20k dataset"
    - "bridging category level and instance level semantic image segmentation"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "places an image database for deep scene understanding"

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)