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: "accelerated training for massive classification via dynamic class selection"
  Abstract: Massive classification, a classification task defined over a vast number of classes (hundreds of thousands or even millions), has become an essential part of many real-world systems, such as face recognition. Existing methods, including the deep networks that achieved remarkable success in recent years, were mostly devised for problems with a moderate number of classes. They would meet with substantial difficulties, e.g. excessive memory demand and computational cost, when applied to massive problems. We present a new method to tackle this problem. This method can efficiently and accurately identify a small number of "active classes" for each mini-batch, based on a set of dynamic class hierarchies constructed on the fly. We also develop an adaptive allocation scheme thereon, which leads to a better tradeoff between performance and cost. On several large-scale benchmarks, our method significantly reduces the training cost and memory demand, while maintaining competitive performance.

Connected through hyperedge HE-1 (induced by source paper "semanticadv generating adversarial examples via attribute conditional image editing"):
  with members:
    - "meshadv adversarial meshes for visual recognition"
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "big but imperceptible adversarial perturbations via semantic manipulation"
    - "spatially transformed adversarial examples"
    - "cosface large margin cosine loss for deep face recognition"
    - "large scale gan training for high fidelity natural image synthesis"
    - "attacks meet interpretability attribute steered detection of adversarial samples"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "softmax dissection towards understanding intra and inter class objective for embedding learning"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "homocentric hypersphere feature embedding for person re identification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep metric learning with angular loss"
    - "l2 constrained softmax loss for discriminative face verification"
    - "level playing field for million scale face recognition"
    - "efficient softmax approximation for gpus"

Connected through hyperedge HE-3 (induced by source paper "learning to cluster faces on an affinity graph"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "face clustering representation and pairwise constraints"
    - "consensus driven propagation in massive unlabeled data for face recognition"
    - "a proximity aware hierarchical clustering of faces"
    - "mask r cnn"
    - "clustering millions of faces by identity"

Connected through hyperedge HE-4 (induced by source paper "consensus driven propagation in massive unlabeled data for face recognition"):
  with members:
    - "the devil of face recognition is in the noise"
    - "arcface additive angular margin loss for deep face recognition"
    - "pose robust face recognition via deep residual equivariant mapping"
    - "deep imbalanced learning for face recognition and attribute prediction"
    - "semi supervised sparse representation based classification for face recognition with insufficient labeled samples"
    - "learning transferable architectures for scalable image recognition"
    - "mix and match tuning for self supervised semantic segmentation"
    - "unsupervised domain adaptation for face recognition in unlabeled videos"

Connected through hyperedge HE-5 (induced by source paper "energy efficient inference accelerator for memory augmented neural networks on an fpga"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "hardware automated datafow deployment of cnns"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "quantized memory augmented neural networks"
    - "finn a framework for fast scalable binarized neural network inference"
    - "an efficient approach to boosting performance of deep spiking network training"
    - "fpga based low power speech recognition with recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "accelerating large scale knowledge distillation via dynamic importance sampling"):
  with members:
    - "large scale distributed neural network training through online distillation"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "distilling a neural network into a soft decision tree"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "exploring the limits of language modeling"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

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)