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: "how to train triplet networks with 100k identities"
  Abstract: Training triplet networks with large-scale data is challenging in face recognition. Due to the number of possible triplets explodes with the number of samples, previous studies adopt the online hard negative mining(OHNM) to handle it. However, as the number of identities becomes extremely large, the training will suffer from bad local minima because effective hard triplets are difficult to be found. To solve the problem, in this paper, we propose training triplet networks with subspace learning, which splits the space of all identities into subspaces consisting of only similar identities. Combined with the batch OHNM, hard triplets can be found much easier. Experiments on the large-scale MS-Celeb-1M challenge with 100K identities demonstrate that the proposed method can largely improve the performance. In addition, to deal with heavy noise and large-scale retrieval, we also make some efforts on robust noise removing and efficient image retrieval, which are used jointly with the subspace learning to obtain the state-of-the-art performance on the MS-Celeb-1M competition (without external data in Challenge1).

Connected through hyperedge HE-1 (induced by source paper "learning with average precision training image retrieval with a listwise loss"):
  with members:
    - "local descriptors optimized for average precision"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "mihash online hashing with mutual information"
    - "in defense of the triplet loss for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "vse improving visual semantic embeddings with hard negatives"
    - "fine tuning cnn image retrieval with no human annotation"
    - "no fuss distance metric learning using proxies"

Connected through hyperedge HE-2 (induced by source paper "bag of negatives for siamese architectures"):
  with members:
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "efficient and deep person re identification using multi level similarity"
    - "deep metric learning with hierarchical triplet loss"
    - "human semantic parsing for person re identification"
    - "attention based ensemble for deep metric learning"
    - "sampling matters in deep embedding learning"

Connected through hyperedge HE-3 (induced by source paper "improving collaborative metric learning with efficient negative sampling"):
  with members:
    - "deep content user embedding model for music recommendation"
    - "word2vec applied to recommendation hyperparameters matter"
    - "sampling matters in deep embedding learning"
    - "in defense of the triplet loss for person re identification"
    - "learning spread out local feature descriptors"
    - "smart mining for deep metric learning"
    - "item2vec neural item embedding for collaborative filtering"
    - "tristounet triplet loss for speaker turn embedding"

Connected through hyperedge HE-4 (induced by source paper "robust re identification of manta rays from natural markings by learning pose invariant embeddings"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "sampling matters in deep embedding learning"
    - "in defense of the triplet loss for person re identification"
    - "deep metric learning with angular loss"
    - "integral curvature representation and matching algorithms for identification of dolphins and whales"
    - "a multi task deep network for person re identification"
    - "facenet a unified embedding for face recognition and clustering"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-5 (induced by source paper "large scale bisample learning on id vs spot face recognition"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "additive margin softmax for face verification"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep hyperspherical learning"

Connected through hyperedge HE-6 (induced by source paper "model distillation with knowledge transfer from face classification to alignment and verification"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "deep model compression distilling knowledge from noisy teachers"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "you only look once unified real time object detection"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "facial landmark detection with tweaked convolutional neural networks"

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)