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 "recurrent embedding aggregation network for video face recognition"):
  Abstract: Recurrent networks have been successful in analyzing temporal data and have been widely used for video analysis. However, for video face recognition, where the base CNNs trained on large-scale data already provide discriminative features, using Long Short-Term Memory (LSTM), a popular recurrent network, for feature learning could lead to overfitting and degrade the performance instead. We propose a Recurrent Embedding Aggregation Network (REAN) for set to set face recognition. Compared with LSTM, REAN is robust against overfitting because it only learns how to aggregate the pre-trained embeddings rather than learning representations from scratch. Compared with quality-aware aggregation methods, REAN can take advantage of the context information to circumvent the noise introduced by redundant video frames. Empirical results on three public domain video face recognition datasets, IJB-S, YTF, and PaSC show that the proposed REAN significantly outperforms naive CNN-LSTM structure and quality-aware aggregation methods.

Includes members:
  Member "dependency aware attention control for unconstrained face recognition with image sets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "multi prototype networks for unconstrained set based face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "face hallucination with finishing touches")
  Member "spatial temporal recurrent neural network for emotion recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "la furca iterative context aware end to end monaural speech separation based on dual path deep parallel inter intra bi lstm with attention")
  Member "video face recognition component wise feature aggregation network c fan":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic face embeddings")
  Member "crystal loss and quality pooling for unconstrained face verification and recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a quadruplet loss for enforcing semantically coherent embeddings in multi output classification problems")
  Member "trunk branch ensemble convolutional neural networks for video based face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dual triplet metric learning for unsupervised domain adaptation in video based face recognition")
  Member "ghostvlad for set based face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "identification of indian languages using ghost vlad pooling")
  Member "multicolumn networks for face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distribution distillation loss generic approach for improving face recognition from hard samples")

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)