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: "learning a metric embedding for face recognition using the multibatch method"
  Abstract: This work is motivated by the engineering task of achieving a near state-of-the-art face recognition on a minimal computing budget running on an embedded system. Our main technical contribution centers around a novel training method, called Multibatch, for similarity learning, i.e., for the task of generating an invariant "face signature" through training pairs of "same" and "not-same" face images. The Multibatch method first generates signatures for a mini-batch of $k$ face images and then constructs an unbiased estimate of the full gradient by relying on all $k^2-k$ pairs from the mini-batch. We prove that the variance of the Multibatch estimator is bounded by $O(1/k^2)$, under some mild conditions. In contrast, the standard gradient estimator that relies on random $k/2$ pairs has a variance of order $1/k$. The smaller variance of the Multibatch estimator significantly speeds up the convergence rate of stochastic gradient descent. Using the Multibatch method we train a deep convolutional neural network that achieves an accuracy of $98.2\%$ on the LFW benchmark, while its prediction runtime takes only $30$msec on a single ARM Cortex A9 core. Furthermore, the entire training process took only 12 hours on a single Titan X GPU.

Connected through hyperedge HE-1 (induced by source paper "neural architecture search for deep face recognition"):
  with members:
    - "sharpdarts faster and more accurate differentiable architecture search"
    - "a performance comparison of loss functions for deep face recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "dropblock a regularization method for convolutional networks"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning towards minimum hyperspherical energy"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-2 (induced by source paper "id aware quality for set based person re identification"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "multi scale 3d convolution network for video based person re identification"
    - "sta spatial temporal attention for large scale video based person re identification"
    - "learn to pay attention"
    - "spatial and temporal mutual promotion for video based person re identification"
    - "video based person re identification with accumulative motion context"
    - "person re identification via recurrent feature aggregation"
    - "re ranking person re identification with k reciprocal encoding"

Connected through hyperedge HE-3 (induced by source paper "minimum margin loss for deep face recognition"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "orthogonal deep features decomposition for age invariant face recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "side information for face completion a robust pca approach"
    - "arcface additive angular margin loss for deep face recognition"
    - "a fast and accurate system for face detection identification and verification"
    - "ring loss convex feature normalization for face recognition"
    - "additive margin softmax for face verification"

Connected through hyperedge HE-4 (induced by source paper "generalization in metric learning should the embedding layer be the embedding layer"):
  with members:
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "attention based ensemble for deep metric learning"
    - "sampling matters in deep embedding learning"
    - "in defense of the triplet loss for person re identification"
    - "focal loss for dense object detection"
    - "deep metric learning with angular loss"
    - "significance of softmax based features in comparison to distance metric learning based features"
    - "fine tuning cnn image retrieval with no human annotation"

Connected through hyperedge HE-5 (induced by source paper "multi task convolutional neural network for pose invariant face recognition"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "reconstruction based disentanglement for pose invariant face recognition"
    - "towards large pose face frontalization in the wild"
    - "cross stitch networks for multi task learning"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "triplet probabilistic embedding for face verification and clustering"
    - "deepid3 face recognition with very deep neural networks"
    - "the multiverse loss for robust transfer learning"

Connected through hyperedge HE-6 (induced by source paper "ring loss convex feature normalization for face recognition"):
  with members:
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "sphereface deep hypersphere embedding for face recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "a proximity aware hierarchical clustering of faces"
    - "cosine normalization using cosine similarity instead of dot product in neural networks"

Connected through hyperedge HE-7 (induced by source paper "deep metric learning by online soft mining and class aware attention"):
  with members:
    - "learn to pay attention"
    - "video based person re identification with accumulative motion context"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "region based quality estimation network for large scale person re identification"
    - "smart mining for deep metric learning"
    - "understanding deep learning requires rethinking generalization"
    - "learning deep embeddings with histogram loss"
    - "hard aware deeply cascaded embedding"

Connected through hyperedge HE-8 (induced by source paper "git loss for deep face recognition"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "neural aggregation network for video face recognition"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "targeting ultimate accuracy face recognition via deep embedding"
    - "sparsifying neural network connections for face recognition"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"

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)