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: "hard negative mining for metric learning based zero shot classification"
  Abstract: Zero-Shot learning has been shown to be an efficient strategy for domain adaptation. In this context, this paper builds on the recent work of Bucher et al. [1], which proposed an approach to solve Zero-Shot classification problems (ZSC) by introducing a novel metric learning based objective function. This objective function allows to learn an optimal embedding of the attributes jointly with a measure of similarity between images and attributes. This paper extends their approach by proposing several schemes to control the generation of the negative pairs, resulting in a significant improvement of the performance and giving above state-of-the-art results on three challenging ZSC datasets.

Connected through hyperedge HE-1 (induced by source paper "rethinking curriculum learning with incremental labels and adaptive compensation"):
  with members:
    - "on the power of curriculum learning in training deep networks"
    - "residual networks of residual networks multilevel residual networks"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "experience replay for continual learning"
    - "learning to teach"
    - "shakedrop regularization for deep residual learning"
    - "drop activation implicit parameter reduction and harmonic regularization"
    - "label refinery improving imagenet classification through label progression"

Connected through hyperedge HE-2 (induced by source paper "a review of object detection models based on convolutional neural network"):
  with members:
    - "advancements in image classification using convolutional neural network"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "understanding batch normalization"
    - "focal loss for dense object detection"
    - "single shot refinement neural network for object detection"
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "mask r cnn"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "manipulating attributes of natural scenes via hallucination"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "a closed form solution to photorealistic image stylization"
    - "multimodal unsupervised image to image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "semi parametric image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-4 (induced by source paper "deep metric learning with hierarchical triplet loss"):
  with members:
    - "sampling matters in deep embedding learning"
    - "sphereface deep hypersphere embedding for face recognition"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "smart mining for deep metric learning"
    - "embedding deep metric for person re identication a study against large variations"
    - "siamese instance search for tracking"
    - "learning deep embeddings with histogram loss"
    - "hard aware deeply cascaded embedding"

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)