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: "margin sample mining loss a deep learning based method for person re identification"
  Abstract: Person re-identification (ReID) is an important task in computer vision. Recently, deep learning with a metric learning loss has become a common framework for ReID. In this paper, we also propose a new metric learning loss with hard sample mining called margin smaple mining loss (MSML) which can achieve better accuracy compared with other metric learning losses, such as triplet loss. In experi- ments, our proposed methods outperforms most of the state-of-the-art algorithms on Market1501, MARS, CUHK03 and CUHK-SYSU.

Connected through hyperedge HE-1 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "beyond human parts dual part aligned representations for person re identification"
    - "aanet attribute attention network for person re identifications"
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "foreground aware pyramid reconstruction for alignment free occluded person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "improving person re identification by attribute and identity learning"

Connected through hyperedge HE-2 (induced by source paper "in defense of the triplet loss again learning robust person re identification with fast approximated triplet loss and label distillation"):
  with members:
    - "a theoretically sound upper bound on the triplet loss for improving the efficiency of deep distance metric learning"
    - "calibrated domain invariant learning for highly generalizable large scale re identification"
    - "the devil of face recognition is in the noise"
    - "not all samples are created equal deep learning with importance sampling"
    - "support neighbor loss for person re identification"
    - "modality distillation with multiple stream networks for action recognition"
    - "iterative learning with open set noisy labels"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-3 (induced by source paper "a unified neural network for object detection multiple object tracking and vehicle re identification"):
  with members:
    - "vehicle re identification an efficient baseline using triplet embedding"
    - "bag of freebies for training object detection neural networks"
    - "fcos fully convolutional one stage object detection"
    - "focal loss for dense object detection"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "cascade r cnn delving into high quality object detection"
    - "dssd deconvolutional single shot detector"
    - "simple online and realtime tracking with a deep association metric"

Connected through hyperedge HE-4 (induced by source paper "stnreid deep convolutional networks with pairwise spatial transformer networks for partial person re identification"):
  with members:
    - "features for multi target multi camera tracking and re identification"
    - "partial person re identification with alignment and hallucination"
    - "recognizing partial biometric patterns"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "in defense of the triplet loss for person re identification"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"
    - "glad global local alignment descriptor for pedestrian retrieval"
    - "alignedreid surpassing human level performance in person re identification"

Connected through hyperedge HE-5 (induced by source paper "cbhe corner based building height estimation for complex street scene images"):
  with members:
    - "triplet center loss for multi view 3d object retrieval"
    - "sparse representation based open set recognition"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "combining maps and street level images for building height and facade estimation"
    - "towards open set deep networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "metric localization using google street view"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "batch dropblock network for person re identification and beyond"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "harmonious attention network for person re identification"
    - "dropblock a regularization method for convolutional networks"
    - "deep randomized ensembles for metric learning"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "re id done right towards good practices for person re identification"

Connected through hyperedge HE-7 (induced by source paper "scpnet spatial channel parallelism network for joint holistic and partial person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "person re identification by camera correlation aware feature augmentation"
    - "harmonious attention network for person re identification"
    - "re id done right towards good practices for person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "in defense of the triplet loss for person re identification"
    - "scalable person re identification on supervised smoothed manifold"

Connected through hyperedge HE-8 (induced by source paper "cross resolution person re identification with deep antithetical learning"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "re ranking person re identification with k reciprocal encoding"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "alignedreid surpassing human level performance in person re identification"
    - "divide and fuse a re ranking approach for person re identification"
    - "neural person search machines"

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)