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: "rethinking feature discrimination and polymerization for large scale recognition"
  Abstract: Feature matters. How to train a deep network to acquire discriminative features across categories and polymerized features within classes has always been at the core of many computer vision tasks, specially for large-scale recognition systems where test identities are unseen during training and the number of classes could be at million scale. In this paper, we address this problem based on the simple intuition that the cosine distance of features in high-dimensional space should be close enough within one class and far away across categories. To this end, we proposed the congenerous cosine (COCO) algorithm to simultaneously optimize the cosine similarity among data. It inherits the softmax property to make inter-class features discriminative as well as shares the idea of class centroid in metric learning. Unlike previous work where the center is a temporal, statistical variable within one mini-batch during training, the formulated centroid is responsible for clustering inner-class features to enforce them polymerized around the network truncus. COCO is bundled with discriminative training and learned end-to-end with stable convergence. Experiments on five benchmarks have been extensively conducted to verify the effectiveness of our approach on both small-scale classification task and large-scale human recognition problem.

Connected through hyperedge HE-1 (induced by source paper "the devil is in the channels mutual channel loss for fine grained image classification"):
  with members:
    - "deep learning for free hand sketch a survey"
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multi graph transformer for free hand sketch recognition"
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "object part attention model for fine grained image classification"
    - "cosface large margin cosine loss for deep face recognition"
    - "learning to navigate for fine grained classification"

Connected through hyperedge HE-2 (induced by source paper "person recognition in personal photo collections"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "pose aware person recognition"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "l2 constrained softmax loss for discriminative face verification"
    - "robust depth based person re identification"
    - "level playing field for million scale face recognition"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "clustering millions of faces by identity"

Connected through hyperedge HE-3 (induced by source paper "biometric recognition using deep learning a survey"):
  with members:
    - "p2sgrad refined gradients for optimizing deep face models"
    - "thirdeye triplet based iris recognition without normalization"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "deepiris iris recognition using a deep learning approach"
    - "fingernet pushing the limits of fingerprint recognition using convolutional neural network"
    - "generating talking face landmarks from speech"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-4 (induced by source paper "learning imbalanced datasets with label distribution aware margin loss"):
  with members:
    - "diversity in faces"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "push the student to learn right progressive gradient correcting by meta learner on corrupted labels"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "overfitting of neural nets under class imbalance analysis and improvements for segmentation"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "regularized learning for domain adaptation under label shifts"

Connected through hyperedge HE-5 (induced by source paper "feature intertwiner for object detection"):
  with members:
    - "finding task relevant features for few shot learning by category traversal"
    - "neural network encapsulation"
    - "gradient harmonized single stage detector"
    - "grid r cnn"
    - "learning monocular depth by distilling cross domain stereo networks"
    - "improving gans using optimal transport"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-6 (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"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-7 (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"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "l2 constrained softmax loss for discriminative face verification"

Connected through hyperedge HE-8 (induced by source paper "rethinking loss design for large scale 3d shape retrieval"):
  with members:
    - "angular triplet center loss for multi view 3d shape retrieval"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "mining point cloud local structures by kernel correlation and graph pooling"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "pairwise decomposition of image sequences for active multi view recognition"
    - "shapenet an information rich 3d model repository"
    - "multi view convolutional neural networks for 3d shape 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)