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: "vehicle re identification an efficient baseline using triplet embedding"
  Abstract: In this paper we tackle the problem of vehicle re-identification in a camera network utilizing triplet embeddings. Re-identification is the problem of matching appearances of objects across different cameras. With the proliferation of surveillance cameras enabling smart and safer cities, there is an ever-increasing need to re-identify vehicles across cameras. Typical challenges arising in smart city scenarios include variations of viewpoints, illumination and self occlusions. Most successful approaches for re-identification involve (deep) learning an embedding space such that the vehicles of same identities are projected closer to one another, compared to the vehicles representing different identities. Popular loss functions for learning an embedding (space) include contrastive or triplet loss. In this paper we provide an extensive evaluation of these losses applied to vehicle re-identification and demonstrate that using the best practices for learning embeddings outperform most of the previous approaches proposed in the vehicle re-identification literature. Compared to most existing state-of-the-art approaches, our approach is simpler and more straightforward for training utilizing only identity-level annotations, along with one of the smallest published embedding dimensions for efficient inference. Furthermore in this work we introduce a formal evaluation of a triplet sampling variant (batch sample) into the re-identification literature.

Connected through hyperedge HE-1 (induced by source paper "uncertainty aware multi shot knowledge distillation for image based object re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "horizontal pyramid matching for person re identification"
    - "densely semantically aligned person re identification"
    - "human semantic parsing for person re identification"

Connected through hyperedge HE-2 (induced by source paper "robust extensible and fast teamed classifiers for vehicle tracking in multi camera networks"):
  with members:
    - "boxcars improving fine grained recognition of vehicles using 3 d bounding boxes in traffic surveillance"
    - "towards universal object detection by domain attention"
    - "assed a framework for identifying physical events through adaptive social sensor data filtering"
    - "domain adaptive attention model for unsupervised cross domain person re identification"
    - "towards real time head pose estimation exploring parameter reduced residual networks on in the wild datasets"
    - "do imagenet classifiers generalize to imagenet"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "cross domain knowledge learning with dual branch adversarial network for vehicle re identification"

Connected through hyperedge HE-3 (induced by source paper "simulating content consistent vehicle datasets with attribute descent"):
  with members:
    - "meta sim learning to generate synthetic datasets"
    - "cityflow a city scale benchmark for multi target multi camera vehicle tracking and re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "moment matching for multi source domain adaptation"
    - "ram a region aware deep model for vehicle re identification"
    - "learning to simulate"
    - "unity a general platform for intelligent agents"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"

Connected through hyperedge HE-4 (induced by source paper "a unified neural network for object detection multiple object tracking and vehicle re identification"):
  with members:
    - "bag of freebies for training object detection neural networks"
    - "fcos fully convolutional one stage object detection"
    - "focal loss for dense object detection"
    - "margin sample mining loss a deep learning based method for person re identification"
    - "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-5 (induced by source paper "stripe based and attribute aware network a two branch deep model for vehicle re identification"):
  with members:
    - "a dual path model with adaptive attention for vehicle re identification"
    - "attributes guided feature learning for vehicle re identification"
    - "vehicle re identification in aerial imagery dataset and approach"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "vehicle re identification using quadruple directional deep learning features"
    - "re id done right towards good practices for person re identification"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "part based deep hashing for large scale person re identification"

Connected through hyperedge HE-6 (induced by source paper "background segmentation for vehicle re identification"):
  with members:
    - "deep cosine metric learning for person re identification"
    - "ram a region aware deep model for vehicle re identification"
    - "yolov3 an incremental improvement"
    - "in defense of the triplet loss for person re identification"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "pyramid scene parsing network"
    - "relay backpropagation for effective learning of deep convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "part guided attention learning for vehicle re identification"):
  with members:
    - "a dual path model with adaptive attention for vehicle re identification"
    - "divide and conquer the embedding space for metric learning"
    - "pvss a progressive vehicle search system for video surveillance networks"
    - "vehicle re identification in aerial imagery dataset and approach"
    - "vehicle re identification using quadruple directional deep learning features"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"

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)