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: "do we need more training data"
  Abstract: Datasets for training object recognition systems are steadily increasing in size. This paper investigates the question of whether existing detectors will continue to improve as data grows, or saturate in performance due to limited model complexity and the Bayes risk associated with the feature spaces in which they operate. We focus on the popular paradigm of discriminatively trained templates defined on oriented gradient features. We investigate the performance of mixtures of templates as the number of mixture components and the amount of training data grows. Surprisingly, even with proper treatment of regularization and "outliers", the performance of classic mixture models appears to saturate quickly ($${\sim }10$$~10 templates and $${\sim }100$$~100 positive training examples per template). This is not a limitation of the feature space as compositional mixtures that share template parameters via parts and that can synthesize new templates not encountered during training yield significantly better performance. Based on our analysis, we conjecture that the greatest gains in detection performance will continue to derive from improved representations and learning algorithms that can make efficient use of large datasets.

Connected through hyperedge HE-1 (induced by source paper "a two stage approach to few shot learning for image recognition"):
  with members:
    - "generalizing from a few examples a survey on few shot learning"
    - "adaptive posterior learning few shot learning with a surprise based memory module"
    - "few shot learning with metric agnostic conditional embeddings"
    - "on first order meta learning algorithms"
    - "sample to sample correspondence for unsupervised domain adaptation"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "unsupervised domain adaptation using regularized hyper graph matching"
    - "meta transfer learning for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "multi level semantic feature augmentation for one shot learning"):
  with members:
    - "deep meta learning learning to learn in the concept space"
    - "low shot learning from imaginary data"
    - "memory matching networks for one shot image recognition"
    - "dynamic few shot visual learning without forgetting"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "meta sgd learning to learn quickly for few shot learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "large scale long tailed recognition in an open world"):
  with members:
    - "model cards for model reporting"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "women also snowboard overcoming bias in captioning models"
    - "arcface additive angular margin loss for deep face recognition"
    - "episodic curiosity through reachability"
    - "incremental few shot learning with attention attractor networks"
    - "low shot learning from imaginary data"
    - "learning adversarially fair and transferable representations"

Connected through hyperedge HE-4 (induced by source paper "object detection at 200 frames per second"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning transferable architectures for scalable image recognition"
    - "focal loss for dense object detection"
    - "data distillation towards omni supervised learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dsod learning deeply supervised object detectors from scratch"
    - "mask r cnn"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (induced by source paper "challenging images for minds and machines"):
  with members:
    - "face recognition via centralized coordinate learning"
    - "densepose dense human pose estimation in the wild"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "east an efficient and accurate scene text detector"
    - "prototypical networks for few shot learning"
    - "failures of gradient based deep learning"
    - "learning visual reasoning without strong priors"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-6 (induced by source paper "human like machine learning limitations and suggestions"):
  with members:
    - "emergence of locomotion behaviours in rich environments"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "to fall or not to fall a visual approach to physical stability prediction"
    - "learning physical intuition of block towers by example"
    - "hopc histogram of oriented principal components of 3d pointclouds for action recognition"

Connected through hyperedge HE-7 (induced by source paper "unsupervised machine learning for networking techniques applications and research challenges"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "a survey of machine and deep learning methods for internet of things iot security"
    - "an introduction to deep learning for the physical layer"
    - "shallow and deep networks intrusion detection system a taxonomy and survey"
    - "iot data analytics using deep learning"
    - "from 4g to 5g self organized network management meets machine learning"
    - "why and how networks should run themselves"

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)