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: "network of experts for large scale image categorization"
  Abstract: We present a tree-structured network architecture for large scale image classification. The trunk of the network contains convolutional layers optimized over all classes. At a given depth, the trunk splits into separate branches, each dedicated to discriminate a different subset of classes. Each branch acts as an expert classifying a set of categories that are difficult to tell apart, while the trunk provides common knowledge to all experts in the form of shared features. The training of our "network of experts" is completely end-to-end: the partition of categories into disjoint subsets is learned simultaneously with the parameters of the network trunk and the experts are trained jointly by minimizing a single learning objective over all classes. The proposed structure can be built from any existing convolutional neural network (CNN). We demonstrate its generality by adapting 4 popular CNNs for image categorization into the form of networks of experts. Our experiments on CIFAR100 and ImageNet show that in every case our method yields a substantial improvement in accuracy over the base CNN, and gives the best result achieved so far on CIFAR100. Finally, the improvement in accuracy comes at little additional cost: compared to the base network, the training time is only moderately increased and the number of parameters is comparable or in some cases even lower.

Connected through hyperedge HE-1 (induced by source paper "continual learning in neural networks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "knowledge transfer in deep block modular neural networks"
    - "meta learning representations for continual learning"
    - "continual reinforcement learning in 3d non stationary environments"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"

Connected through hyperedge HE-2 (induced by source paper "making better mistakes leveraging class hierarchies with deep networks"):
  with members:
    - "integrating domain knowledge using hierarchies to improve deep classifiers"
    - "when does label smoothing help"
    - "hierarchy based image embeddings for semantic image retrieval"
    - "class balanced loss based on effective number of samples"
    - "theoretically principled trade off between robustness and accuracy"
    - "on the convergence of adam and beyond"
    - "do convolutional neural networks learn class hierarchy"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (induced by source paper "decision propagation networks for image classification"):
  with members:
    - "cinic 10 is not imagenet or cifar 10"
    - "ensemble soft margin softmax loss for image classification"
    - "class2str end to end latent hierarchy learning"
    - "do convolutional neural networks learn class hierarchy"
    - "deep forest towards an alternative to deep neural networks"
    - "deep convolutional decision jungle for image classification"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "improving pairwise ranking for multi label image classification"

Connected through hyperedge HE-4 (induced by source paper "expert sample consensus applied to camera re localization"):
  with members:
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "geometric loss functions for camera pose regression with deep learning"
    - "exploiting points and lines in regression forests for rgb d camera relocalization"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "geometry aware learning of maps for camera localization"
    - "backtracking regression forests for accurate camera relocalization"
    - "learning to find good correspondences"

Connected through hyperedge HE-5 (induced by source paper "expertmatcher automating ml model selection for clients using hidden representations"):
  with members:
    - "expertmatcher automating ml model selection for users in resource constrained countries"
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "no peek a survey of private distributed deep learning"
    - "flash photography for data driven hidden scene recovery"
    - "expert gate lifelong learning with a network of experts"

Connected through hyperedge HE-6 (induced by source paper "expertmatcher automating ml model selection for users in resource constrained countries"):
  with members:
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"
    - "no peek a survey of private distributed deep learning"
    - "flash photography for data driven hidden scene recovery"
    - "expert gate lifelong learning with a network of experts"

Connected through hyperedge HE-7 (induced by source paper "learning to compose topic aware mixture of experts for zero shot video captioning"):
  with members:
    - "neural baby talk"
    - "universal neural machine translation for extremely low resource languages"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "s3d single shot multi span detector via fully 3d convolutional networks"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "tafe net task aware feature embeddings for efficient learning and inference"
    - "decoupled novel object captioner"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"

Connected through hyperedge HE-8 (induced by source paper "multi level factorisation net for person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "harmonious attention network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "scalable person re identification on supervised smoothed manifold"
    - "deep mutual learning"
    - "re ranking person re identification with k reciprocal encoding"
    - "person re identification by deep joint learning of multi loss classification"

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)