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: "neural networks regularization through class wise invariant representation learning"
  Abstract: Training deep neural networks is known to require a large number of training samples. However, in many applications only few training samples are available. In this work, we tackle the issue of training neural networks for classification task when few training samples are available. We attempt to solve this issue by proposing a new regularization term that constrains the hidden layers of a network to learn class-wise invariant representations. In our regularization framework, learning invariant representations is generalized to the class membership where samples with the same class should have the same representation. Numerical experiments over MNIST and its variants showed that our proposal helps improving the generalization of neural network particularly when trained with few samples. We provide the source code of our framework this https URL .

Connected through hyperedge HE-1 (induced by source paper "topologically densified distributions"):
  with members:
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "a kernel theory of modern data augmentation"
    - "utilizing class information for deep network representation shaping"
    - "augment your batch better training with larger batches"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "three mechanisms of weight decay regularization"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised object localization using min max entropy an interpretable framework"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "min entropy latent model for weakly supervised object detection"
    - "constrained cnn losses for weakly supervised segmentation"
    - "visual interpretability for deep learning a survey"
    - "dropblock a regularization method for convolutional networks"
    - "attention based deep multiple instance learning"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"

Connected through hyperedge HE-3 (induced by source paper "min max entropy for weakly supervised pointwise localization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "min entropy latent model for weakly supervised object detection"
    - "constrained cnn losses for weakly supervised segmentation"
    - "visual interpretability for deep learning a survey"
    - "dropblock a regularization method for convolutional networks"
    - "attention based deep multiple instance learning"
    - "adversarial complementary learning for weakly supervised object localization"
    - "tell me where to look guided attention inference network"

Connected through hyperedge HE-4 (induced by source paper "learning representations for neural network based classification using the information bottleneck principle"):
  with members:
    - "understanding individual neuron importance using information theory"
    - "uncertainty in the variational information bottleneck"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "i revnet deep invertible networks"
    - "estimating information flow in neural networks"
    - "the variational deficiency bottleneck"
    - "emergence of invariance and disentanglement in deep representations"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-5 (induced by source paper "utilizing class information for dnn representation shaping"):
  with members:
    - "on the statistical and information theoretic characteristics of deep network representations"
    - "learning structured sparsity in deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "learning discriminative features with class encoder"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "reducing overfitting in deep networks by decorrelating representations"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)