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 deep features via congenerous cosine loss for person recognition"
  Abstract: Person recognition aims at recognizing the same identity across time and space with complicated scenes and similar appearance. In this paper, we propose a novel method to address this task by training a network to obtain robust and representative features. The intuition is that we directly compare and optimize the cosine distance between two features - enlarging inter-class distinction as well as alleviating inner-class variance. We propose a congenerous cosine loss by minimizing the cosine distance between samples and their cluster centroid in a cooperative way. Such a design reduces the complexity and could be implemented via softmax with normalized inputs. Our method also differs from previous work in person recognition that we do not conduct a second training on the test subset. The identity of a person is determined by measuring the similarity from several body regions in the reference set. Experimental results show that the proposed approach achieves better classification accuracy against previous state-of-the-arts.

Connected through hyperedge HE-1 (induced by source paper "a performance comparison of loss functions for deep face recognition"):
  with members:
    - "minimum margin loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "sphereface deep hypersphere embedding for face recognition"
    - "focal loss for dense object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "l2 constrained softmax loss for discriminative face verification"

Connected through hyperedge HE-2 (induced by source paper "adacos adaptively scaling cosine logits for effectively learning deep face representations"):
  with members:
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "ring loss convex feature normalization for face recognition"
    - "additive margin softmax for face verification"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "rethinking feature discrimination and polymerization for large scale recognition"

Connected through hyperedge HE-3 (induced by source paper "p2sgrad refined gradients for optimizing deep face models"):
  with members:
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "rethinking feature discrimination and polymerization for large scale recognition"
    - "l2 constrained softmax loss for discriminative face verification"

Connected through hyperedge HE-4 (induced by source paper "fix your features stationary and maximally discriminative embeddings using regular polytope fixed classifier networks"):
  with members:
    - "decoupled networks"
    - "fix your classifier the marginal value of training the last weight layer"
    - "learning towards minimum hyperspherical energy"
    - "additive margin softmax for face verification"
    - "improving generalization via scalable neighborhood component analysis"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-5 (induced by source paper "towards interpretable face recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "representation learning by rotating your faces"
    - "on learning 3d face morphable model from in the wild images"
    - "gait recognition via disentangled representation learning"
    - "disentangling features in 3d face shapes for joint face reconstruction and recognition"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "multicolumn networks for face recognition"

Connected through hyperedge HE-6 (induced by source paper "support vector guided softmax loss for face recognition"):
  with members:
    - "the devil of face recognition is in the noise"
    - "ensemble soft margin softmax loss for image classification"
    - "cosface large margin cosine loss for deep face recognition"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "arcface additive angular margin loss for deep face recognition"
    - "ring loss convex feature normalization for face recognition"
    - "additive margin softmax for face verification"
    - "normface l 2 hypersphere embedding for face verification"

Connected through hyperedge HE-7 (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"
    - "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"
    - "range loss for deep face recognition with long tail"

Connected through hyperedge HE-8 (induced by source paper "deep class wise hashing semantics preserving hashing via class wise loss"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "deep supervised discrete hashing"
    - "hashnet deep learning to hash by continuation"
    - "supervised deep hashing for hierarchical labeled data"
    - "fast training of triplet based deep binary embedding networks"
    - "deep supervised hashing with triplet labels"
    - "a survey on learning to hash"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"

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)