human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "circle loss a unified perspective of pair similarity optimization"):
  Abstract: This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity $s_p$ and minimize the between-class similarity $s_n$. We find a majority of loss functions, including the triplet loss and the softmax plus cross-entropy loss, embed $s_n$ and $s_p$ into similarity pairs and seek to reduce $(s_n-s_p)$. Such an optimization manner is inflexible, because the penalty strength on every single similarity score is restricted to be equal. Our intuition is that if a similarity score deviates far from the optimum, it should be emphasized. To this end, we simply re-weight each similarity to highlight the less-optimized similarity scores. It results in a Circle loss, which is named due to its circular decision boundary. The Circle loss has a unified formula for two elemental deep feature learning approaches, i.e. learning with class-level labels and pair-wise labels. Analytically, we show that the Circle loss offers a more flexible optimization approach towards a more definite convergence target, compared with the loss functions optimizing $(s_n-s_p)$. Experimentally, we demonstrate the superiority of the Circle loss on a variety of deep feature learning tasks. On face recognition, person re-identification, as well as several fine-grained image retrieval datasets, the achieved performance is on par with the state of the art.

Includes members:
  Member "joint discriminative and generative learning for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "multi similarity loss with general pair weighting for deep metric learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "adacos adaptively scaling cosine logits for effectively learning deep face representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalizing face representation with unlabeled data")
  Member "softmax dissection towards understanding intra and inter class objective for embedding learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep speaker embeddings for far field speaker recognition on short utterances")
  Member "learning discriminative features with multiple granularities for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning")
  Member "deep metric learning with bier boosting independent embeddings robustly":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "cosface large margin cosine loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "arcface additive angular margin loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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)