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 "a quadruplet loss for enforcing semantically coherent embeddings in multi output classification problems"):
  Abstract: This paper describes one objective function for learning semantically coherent feature embeddings in multi-output classification problems, i.e., when the response variables have dimension higher than one. In particular, we consider the problems of identity retrieval and soft biometrics in visual surveillance environments, which have been attracting growing interests. Inspired by the triplet loss function, we propose a generalization of that concept: a quadruplet loss, that 1) defines a metric that analyzes the number of agreeing labels between pairs of elements; and 2) disregards the notion of anchor, replacing d(A1,A2) < d(A1,B) by d(A,B) < d(C,D) distance constraints, according to such perceived semantic similarity between the elements of each pair. Inherited from the triplet loss formulation, our proposal also privileges small distances between positive pairs, but also explicitly enforces that the distances between negative pairs directly correspond to their similarity in terms of the number of agreeing labels. This typically yields feature embeddings with a strong correspondence between the classes centroids and their semantic descriptions, i.e., where elements that share some of the labels are closer to each other in the destiny space than elements with fully disjoint classes membership. Also, in opposition to its triplet counterpart, the proposed loss is not particularly sensitive to the way learning pairs are mined, being agnostic with regard to demanding criteria for mining learning instances (such as the semi-hard pairs of triplet loss). Our experiments were carried out in four different datasets (BIODI, LFW, Megaface and PETA) and validate our assumptions, showing highly promising results.

Includes members:
  Member "crystal loss and quality pooling for unconstrained face verification and recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "single unit status in deep convolutional neural network codes for face identification sparseness redefined")
  Member "arcface additive angular margin loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "attention set based metric learning for video face recognition":
    also connected through other hyperedges:
      (none)
  Member "beyond triplet loss a deep quadruplet network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "memorizing comprehensively to learn adaptively unsupervised cross domain person re id with multi level memory")
  Member "ssd single shot multibox detector":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "convolutional neural networks for attribute based active authentication on mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning deep features for one class classification")
  Member "facenet a unified embedding for face recognition and clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "the megaface benchmark 1 million faces for recognition at scale":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)