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: "connectivity learning in multi branch networks"
  Abstract: While much of the work in the design of convolutional networks over the last five years has revolved around the empirical investigation of the importance of depth, filter sizes, and number of feature channels, recent studies have shown that branching, i.e., splitting the computation along parallel but distinct threads and then aggregating their outputs, represents a new promising dimension for significant improvements in performance. To combat the complexity of design choices in multi-branch architectures, prior work has adopted simple strategies, such as a fixed branching factor, the same input being fed to all parallel branches, and an additive combination of the outputs produced by all branches at aggregation points. In this work we remove these predefined choices and propose an algorithm to learn the connections between branches in the network. Instead of being chosen a priori by the human designer, the multi-branch connectivity is learned simultaneously with the weights of the network by optimizing a single loss function defined with respect to the end task. We demonstrate our approach on the problem of multi-class image classification using four different datasets where it yields consistently higher accuracy compared to the state-of-the-art ``ResNeXt'' multi-branch network given the same learning capacity.

Connected through hyperedge HE-1 (induced by source paper "assemblenet searching for multi stream neural connectivity in video architectures"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "holistic large scale video understanding"
    - "long term feature banks for detailed video understanding"
    - "charades ego a large scale dataset of paired third and first person videos"
    - "moments in time dataset one million videos for event understanding"
    - "regularized evolution for image classifier architecture search"
    - "videos as space time region graphs"
    - "slowfast networks for video recognition"

Connected through hyperedge HE-2 (induced by source paper "one shot neural architecture search through a posteriori distribution guided sampling"):
  with members:
    - "single path one shot neural architecture search with uniform sampling"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-3 (induced by source paper "differentiable sparsification for deep neural networks"):
  with members:
    - "darts differentiable architecture search"
    - "skipnet learning dynamic routing in convolutional networks"
    - "evolving deep neural networks"
    - "learning transferable architectures for scalable image recognition"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"
    - "aggregated residual transformations for deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-4 (induced by source paper "competitive inner imaging squeeze and excitation for residual network"):
  with members:
    - "multi attention multi class constraint for fine grained image recognition"
    - "convolutional neural networks with alternately updated clique"
    - "learning what and where to attend"
    - "neural ordinary differential equations"
    - "cbam convolutional block attention module"
    - "skipnet learning dynamic routing in convolutional networks"
    - "learning transferable architectures for scalable image recognition"
    - "interleaved group convolutions for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "mobilenetv2 inverted residuals and linear bottlenecks"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "large scale evolution of image classifiers"
    - "learning transferable architectures for scalable image recognition"
    - "learning time efficient deep architectures with budgeted super networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "the power of sparsity in convolutional neural networks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "darts differentiable architecture search"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "efficient neural architecture search via parameter sharing"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"

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)