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 "triplet probabilistic embedding for face verification and clustering"):
  Abstract: Despite significant progress made over the past twenty five years, unconstrained face verification remains a challenging problem. This paper proposes an approach that couples a deep CNN-based approach with a low-dimensional discriminative embedding step, learned using triplet probability constraints to address the unconstrained face verification problem. Aside from yielding performance improvements, this embedding provides significant advantages in terms of memory and for post-processing operations like subject specific clustering. Experiments on the challenging IJB-A dataset show that the proposed algorithm performs close to the state of the art methods in verification and identification metrics, while requiring much less training data and training/test time. The superior performance of the proposed method on the CFP dataset shows that the representation learned by our deep CNN is robust to large pose variation. Furthermore, we demonstrate the robustness of deep features to challenges including age, pose, blur and clutter by performing simple clustering experiments on both IJB-A and LFW datasets.

Includes members:
  Member "neural aggregation network for video face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards universal representation learning for deep face recognition")
  Member "template adaptation for face verification and identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dependency aware attention control for unconstrained face recognition with image sets")
  Member "clustering millions of faces by identity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "person recognition in personal photo collections")
  Member "do we really need to collect millions of faces for effective face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalizing face representation with unlabeled data")
  Member "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "two stream aural visual affect analysis in the wild")
  Member "face search at scale 80 million gallery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dependency aware attention control for unconstrained face recognition with image sets")
  Member "unconstrained face verification using deep cnn features":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "person recognition in personal photo collections")
  Member "learning transferable features with deep adaptation networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on contextual embeddings")

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)