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: "on invariance and selectivity in representation learning"
  Abstract: We discuss data representation which can be learned automatically from data, are invariant to transformations, and at the same time selective, in the sense that two points have the same representation only if they are one the transformation of the other. The mathematical results here sharpen some of the key claims of i-theory -- a recent theory of feedforward processing in sensory cortex.

Connected through hyperedge HE-1 (induced by source paper "are we done with object recognition the icub robot s perspective"):
  with members:
    - "robotic tactile perception of object properties a review"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deep image retrieval learning global representations for image search"
    - "improved deep learning of object category using pose information"
    - "the curious robot learning visual representations via physical interactions"
    - "physically based rendering for indoor scene understanding using convolutional neural networks"
    - "a deep representation for depth images from synthetic data"
    - "learning to recognize objects by retaining other factors of variation"

Connected through hyperedge HE-2 (induced by source paper "learning from brains how to regularize machines"):
  with members:
    - "similarity of neural network representations revisited"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "a neurobiological evaluation metric for neural network model search"
    - "towards the first adversarially robust neural network model on mnist"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "generalisation in humans and deep neural networks"
    - "sparsefool a few pixels make a big difference"

Connected through hyperedge HE-3 (induced by source paper "minimal images in deep neural networks fragile object recognition in natural images"):
  with members:
    - "adversarial examples that fool both computer vision and time limited humans"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "exploring the landscape of spatial robustness"
    - "object recognition with and without objects"
    - "understanding deep learning requires rethinking generalization"
    - "yolo9000 better faster stronger"
    - "delving into transferable adversarial examples and black box attacks"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "modularity matters learning invariant relational reasoning tasks"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "a simple neural network module for relational reasoning"
    - "aggregated residual transformations for deep neural networks"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "hierarchical question image co attention for visual question answering"
    - "deep compositional question answering with neural module networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "universal approximations of invariant maps by neural networks"):
  with members:
    - "polar transformer networks"
    - "rotation equivariant vector field networks"
    - "analysis and design of convolutional networks via hierarchical tensor decompositions"
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "warped convolutions efficient invariance to spatial transformations"
    - "group equivariant convolutional networks"
    - "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "towards a topological geometrical theory of group equivariant non expansive operators for data analysis and machine learning"):
  with members:
    - "exposition and interpretation of the topology of neural networks"
    - "point convolutional neural networks by extension operators"
    - "dynamic routing between capsules"
    - "harmonic networks deep translation and rotation equivariance"
    - "group equivariant convolutional networks"
    - "deep learning and the information bottleneck principle"
    - "convolutional neural networks for sentence classification"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-7 (induced by source paper "variational inference of disentangled latent concepts from unlabeled observations"):
  with members:
    - "unsupervised learning of disentangled representations from video"
    - "towards deeper understanding of variational autoencoding models"
    - "local group invariant representations via orbit embeddings"
    - "understanding visual concepts with continuation learning"
    - "a survey of inductive biases for factorial representation learning"
    - "adversarial feature learning"
    - "deep feature consistent variational autoencoder"
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"

Connected through hyperedge HE-8 (induced by source paper "mathematics of deep learning"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "sharp minima can generalize for deep nets"
    - "geometric deep learning going beyond euclidean data"
    - "opening the black box of deep neural networks via information"
    - "deep relaxation partial differential equations for optimizing deep neural networks"
    - "deep variational information bottleneck"
    - "understanding deep learning requires rethinking generalization"
    - "entropy sgd biasing gradient descent into wide valleys"

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)