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: "why m heads are better than one training a diverse ensemble of deep networks"
  Abstract: Convolutional Neural Networks have achieved state-of-the-art performance on a wide range of tasks. Most benchmarks are led by ensembles of these powerful learners, but ensembling is typically treated as a post-hoc procedure implemented by averaging independently trained models with model variation induced by bagging or random initialization. In this paper, we rigorously treat ensembling as a first-class problem to explicitly address the question: what are the best strategies to create an ensemble? We first compare a large number of ensembling strategies, and then propose and evaluate novel strategies, such as parameter sharing (through a new family of models we call TreeNets) as well as training under ensemble-aware and diversity-encouraging losses. We demonstrate that TreeNets can improve ensemble performance and that diverse ensembles can be trained end-to-end under a unified loss, achieving significantly higher "oracle" accuracies than classical ensembles.

Connected through hyperedge HE-1 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost"):
  with members:
    - "adaptative inference cost with convolutional neural mixture models"
    - "depth adaptive transformer"
    - "hydra an ensemble of convolutional neural networks for geospatial land classification"
    - "robust training with ensemble consensus"
    - "deep tree learning for zero shot face anti spoofing"
    - "improved techniques for training adaptive deep networks"
    - "searching for mobilenetv3"
    - "universally slimmable networks and improved training techniques"

Connected through hyperedge HE-2 (induced by source paper "mothernets rapid deep ensemble learning"):
  with members:
    - "self training ensemble networks for zero shot image recognition"
    - "efficient architecture search by network transformation"
    - "snapshot ensembles train 1 get m for free"
    - "less is more a comprehensive framework for the number of components of ensemble classifiers"
    - "deep networks with stochastic depth"
    - "stochastic multiple choice learning for training diverse deep ensembles"
    - "learning without forgetting"
    - "wide residual networks"

Connected through hyperedge HE-3 (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"
    - "maskconnect connectivity learning by gradient descent"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-4 (induced by source paper "mixture dense regression for object detection and human pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "lvis a dataset for large vocabulary instance segmentation"
    - "overcoming limitations of mixture density networks a sampling and fitting framework for multimodal future prediction"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "feature selective anchor free module for single shot object detection"
    - "unsupervised discovery of parts structure and dynamics"
    - "pifpaf composite fields for human pose estimation"
    - "scale aware trident networks for object detection"

Connected through hyperedge HE-5 (induced by source paper "towards oracle knowledge distillation with neural architecture search"):
  with members:
    - "relational knowledge distillation"
    - "detnas backbone search for object detection"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "paraphrasing complex network network compression via factor transfer"
    - "boosting self supervised learning via knowledge transfer"
    - "knowledge distillation by on the fly native ensemble"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "neural architecture optimization"

Connected through hyperedge HE-6 (induced by source paper "a hierarchical probabilistic u net for modeling multi scale ambiguities"):
  with members:
    - "panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "tensormask a foundation for dense object segmentation"
    - "instance segmentation of biological images using harmonic embeddings"
    - "semantic image synthesis with spatially adaptive normalization"
    - "deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy"
    - "instance segmentation by deep coloring"

Connected through hyperedge HE-7 (induced by source paper "ensemblenet end to end optimization of multi headed models"):
  with members:
    - "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"
    - "knowledge distillation by on the fly native ensemble"
    - "deep mixture of experts via shallow embedding"
    - "large scale distributed neural network training through online distillation"
    - "tafe net task aware feature embeddings for efficient learning and inference"
    - "slowfast networks for video recognition"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep mutual learning"

Connected through hyperedge HE-8 (induced by source paper "diversity in machine learning"):
  with members:
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "tagging like humans diverse and distinct image annotation"
    - "improving sequential determinantal point processes for supervised video summarization"
    - "diversity promoting bayesian learning of latent variable models"
    - "learning detection with diverse proposals"
    - "diversified texture synthesis with feed forward networks"
    - "stochastic multiple choice learning for training diverse deep ensembles"
    - "an ensemble diversity approach to supervised binary hashing"

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)