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: "maskconnect connectivity learning by gradient descent"
  Abstract: Although deep networks have recently emerged as the model of choice for many computer vision problems, in order to yield good results they often require time-consuming architecture search. To combat the complexity of design choices, prior work has adopted the principle of modularized design which consists in defining the network in terms of a composition of topologically identical or similar building blocks (a.k.a. modules). This reduces architecture search to the problem of determining the number of modules to compose and how to connect such modules. Again, for reasons of design complexity and training cost, previous approaches have relied on simple rules of connectivity, e.g., connecting each module to only the immediately preceding module or perhaps to all of the previous ones. Such simple connectivity rules are unlikely to yield the optimal architecture for the given problem.

Connected through hyperedge HE-1 (induced by source paper "autogan neural architecture search for generative adversarial networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "controllable artistic text style transfer via shape matching gan"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "enlightengan deep light enhancement without paired supervision"
    - "the gan landscape losses architectures regularization and normalization"
    - "improving mmd gan training with repulsive loss function"
    - "the relativistic discriminator a key element missing from standard gan"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-2 (induced by source paper "attentive single tasking of multiple tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "taskonomy disentangling task transfer learning"
    - "unified perceptual parsing for scene understanding"
    - "efficient video object segmentation via network modulation"

Connected through hyperedge HE-3 (induced by source paper "autodispnet improving disparity estimation with automl"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "segstereo exploiting semantic information for disparity estimation"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "graph hypernetworks for neural architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "learning to design rna"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "darts differentiable architecture search"

Connected through hyperedge HE-4 (induced by source paper "adaptative inference cost with convolutional neural mixture models"):
  with members:
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "amc automl for model compression and acceleration on mobile devices"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-5 (induced by source paper "evaluating the search phase of neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "on network design spaces for visual recognition"
    - "detnas backbone search for object detection"
    - "exploring randomly wired neural networks for image recognition"
    - "the evolved transformer"
    - "nas bench 101 towards reproducible neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "automated machine learning state of the art and open challenges"):
  with members:
    - "learning internal representations"
    - "meta learning a survey"
    - "autostacker a compositional evolutionary learning system"
    - "regularized evolution for image classifier architecture search"
    - "rafiki machine learning as an analytics service system"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"

Connected through hyperedge HE-7 (induced by source paper "sparsemask differentiable connectivity learning for dense image prediction"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "deeply supervised salient object detection with short connections"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning a discriminative feature network for semantic segmentation"
    - "sparsely aggregated convolutional networks"
    - "path aggregation network for instance segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "context encoding for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "deep elastic networks with model selection for multi task learning"):
  with members:
    - "deep virtual networks for memory efficient inference of multiple tasks"
    - "multi task zipping via layer wise neuron sharing"
    - "a novel channel pruning method for deep neural network compression"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "focal loss for dense object detection"
    - "blockdrop dynamic inference paths in residual networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

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)