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: "batch kalman normalization towards training deep neural networks with micro batches"
  Abstract: As an indispensable component, Batch Normalization (BN) has successfully improved the training of deep neural networks (DNNs) with mini-batches, by normalizing the distribution of the internal representation for each hidden layer. However, the effectiveness of BN would diminish with scenario of micro-batch (e.g., less than 10 samples in a mini-batch), since the estimated statistics in a mini-batch are not reliable with insufficient samples. In this paper, we present a novel normalization method, called Batch Kalman Normalization (BKN), for improving and accelerating the training of DNNs, particularly under the context of micro-batches. Specifically, unlike the existing solutions treating each hidden layer as an isolated system, BKN treats all the layers in a network as a whole system, and estimates the statistics of a certain layer by considering the distributions of all its preceding layers, mimicking the merits of Kalman Filtering. BKN has two appealing properties. First, it enables more stable training and faster convergence compared to previous works. Second, training DNNs using BKN performs substantially better than those using BN and its variants, especially when very small mini-batches are presented. On the image classification benchmark of ImageNet, using BKN powered networks we improve upon the best-published model-zoo results: reaching 74.0% top-1 val accuracy for InceptionV2. More importantly, using BKN achieves the comparable accuracy with extremely smaller batch size, such as 64 times smaller on CIFAR-10/100 and 8 times smaller on ImageNet.

Connected through hyperedge HE-1 (induced by source paper "exemplar normalization for learning deep representation"):
  with members:
    - "channel equilibrium networks for learning deep representation"
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "switchable whitening for deep representation learning"
    - "eca net efficient channel attention for deep convolutional neural networks"
    - "differentiable learning to group channels via groupable convolutional neural networks"
    - "attentive normalization"
    - "positional normalization"
    - "global second order pooling convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "kalman meets bellman improving policy evaluation through value tracking"):
  with members:
    - "a kernel loss for solving the bellman equation"
    - "directly estimating the variance of the lambda return using temporal difference methods"
    - "successor uncertainties exploration and uncertainty in temporal difference learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "shallow updates for deep reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "the uncertainty bellman equation and exploration"
    - "a distributional perspective on reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "adaptively connected neural networks"):
  with members:
    - "discovering underlying person structure pattern with relative local distance for person re identification"
    - "spatial temporal person re identification"
    - "m2m gan many to many generative adversarial transfer learning for person re identification"
    - "occluded person re identification"
    - "large scale learnable graph convolutional networks"
    - "deformable convnets v2 more deformable better results"
    - "graph based global reasoning networks"
    - "random erasing data augmentation"

Connected through hyperedge HE-4 (induced by source paper "switchable whitening for deep representation learning"):
  with members:
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "learning to adapt structured output space for semantic segmentation"
    - "group normalization"
    - "do normalization layers in a deep convnet really need to be distinct"
    - "differentiable learning to normalize via switchable normalization"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "two at once enhancing learning and generalization capacities via ibn net"

Connected through hyperedge HE-5 (induced by source paper "tradi tracking deep neural network weight distributions"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "lca loss change allocation for neural network training"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "deep learning under privileged information using heteroscedastic dropout"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "ionet learning to cure the curse of drift in inertial odometry"

Connected through hyperedge HE-6 (induced by source paper "trust region value optimization using kalman filtering"):
  with members:
    - "shallow updates for deep reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "a distributional perspective on reinforcement learning"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "an overview of gradient descent optimization algorithms"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "differentiable learning to normalize via switchable normalization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "switchable whitening for deep representation learning"
    - "do normalization layers in a deep convnet really need to be distinct"
    - "darts differentiable architecture search"
    - "towards understanding regularization in batch normalization"
    - "decorrelated batch normalization"
    - "efficient neural architecture search via parameter sharing"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-8 (induced by source paper "batch normalization sampling"):
  with members:
    - "l1 norm batch normalization for efficient training of deep neural networks"
    - "scalable methods for 8 bit training of neural networks"
    - "group normalization"
    - "structurally sparsified backward propagation for faster long short term memory training"
    - "understanding batch normalization"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "a novel channel pruning method for deep neural network compression"
    - "imagenet training in minutes"

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)