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: "local relation networks for image recognition"
  Abstract: The convolution layer has been the dominant feature extractor in computer vision for years. However, the spatial aggregation in convolution is basically a pattern matching process that applies fixed filters which are inefficient at modeling visual elements with varying spatial distributions. This paper presents a new image feature extractor, called the local relation layer, that adaptively determines aggregation weights based on the compositional relationship of local pixel pairs. With this relational approach, it can composite visual elements into higher-level entities in a more efficient manner that benefits semantic inference. A network built with local relation layers, called the Local Relation Network (LR-Net), is found to provide greater modeling capacity than its counterpart built with regular convolution on large-scale recognition tasks such as ImageNet classification.

Connected through hyperedge HE-1 (induced by source paper "neuroevolution of self interpretable agents"):
  with members:
    - "rotation translation and cropping for zero shot generalization"
    - "skin lesion classification using cnns with patch based attention and diagnosis guided loss weighting"
    - "improving deep neuroevolution via deep innovation protection"
    - "investigating generalisation in continuous deep reinforcement learning"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "emergent systematic generalization in a situated agent"
    - "continual learning with hypernetworks"
    - "learning to generalize from sparse and underspecified rewards"

Connected through hyperedge HE-2 (induced by source paper "deep image spatial transformation for person image generation"):
  with members:
    - "multi channel attention selection gans for guided image to image translation"
    - "progressive pose attention transfer for person image generation"
    - "unsupervised person image generation with semantic parsing transformation"
    - "liquid warping gan a unified framework for human motion imitation appearance transfer and novel view synthesis"
    - "linearized multi sampling for differentiable image transformation"
    - "dense intrinsic appearance flow for human pose transfer"
    - "structureflow image inpainting via structure aware appearance flow"
    - "cycle in cycle generative adversarial networks for keypoint guided image generation"

Connected through hyperedge HE-3 (induced by source paper "stand alone self attention in vision models"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "nas fpn learning scalable feature pyramid architecture for object detection"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "self attention with relative position representations"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "affine self convolution"):
  with members:
    - "deep scale spaces equivariance over scale"
    - "equivariant multi view networks"
    - "stand alone self attention in vision models"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "attention augmented convolutional networks"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "3d g cnns for pulmonary nodule detection"
    - "cubenet equivariance to 3d rotation and translation"

Connected through hyperedge HE-5 (induced by source paper "style based variational autoencoder for real world super resolution"):
  with members:
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "image super resolution using very deep residual channel attention networks"
    - "residual dense network for image super resolution"
    - "mine mutual information neural estimation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "non local neural networks"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"

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)