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: "geodesics of learned representations"
  Abstract: We develop a new method for visualizing and refining the invariances of learned representations. Specifically, we test for a general form of invariance, linearization, in which the action of a transformation is confined to a low-dimensional subspace. Given two reference images (typically, differing by some transformation), we synthesize a sequence of images lying on a path between them that is of minimal length in the space of the representation (a "representational geodesic"). If the transformation relating the two reference images is linearized by the representation, this sequence should follow the gradual evolution of this transformation. We use this method to assess the invariance properties of a state-of-the-art image classification network and find that geodesics generated for image pairs differing by translation, rotation, and dilation do not evolve according to their associated transformations. Our method also suggests a remedy for these failures, and following this prescription, we show that the modified representation is able to linearize a variety of geometric image transformations.

Connected through hyperedge HE-1 (induced by source paper "untangling in invariant speech recognition"):
  with members:
    - "analyzing learned representations of a deep asr performance prediction model"
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"
    - "deep speaker an end to end neural speaker embedding system"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "convergent learning do different neural networks learn the same representations"

Connected through hyperedge HE-2 (induced by source paper "making convolutional networks shift invariant again"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "using pre training can improve model robustness and uncertainty"
    - "spatially transformed adversarial examples"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"
    - "pooling is neither necessary nor sufficient for appropriate deformation stability in cnns"
    - "why do deep convolutional networks generalize so poorly to small image transformations"

Connected through hyperedge HE-3 (induced by source paper "e lpips robust perceptual image similarity via random transformation ensembles"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "eigen distortions of hierarchical representations"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "pixel recursive super resolution"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "why do deep convolutional networks generalize so poorly to small image transformations"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "making convolutional networks shift invariant again"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "studying invariances of trained convolutional neural networks"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "exploring the space of black box attacks on deep neural networks"
    - "the elephant in the room"

Connected through hyperedge HE-5 (induced by source paper "towards metamerism via foveated style transfer"):
  with members:
    - "sideeye a generative neural network based simulator of human peripheral vision"
    - "eigen distortions of hierarchical representations"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"
    - "object detection through search with a foveated visual system"
    - "perceptually optimized image rendering"
    - "perceptual losses for real time style transfer and super resolution"

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)