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: "part based deep hashing for large scale person re identification"
  Abstract: Large-scale is a trend in person re-identi- fication (re-id). It is important that real-time search be performed in a large gallery. While previous methods mostly focus on discriminative learning, this paper makes the attempt in integrating deep learning and hashing into one framework to evaluate the efficiency and accuracy for large-scale person re-id. We integrate spatial information for discriminative visual representation by partitioning the pedestrian image into horizontal parts. Specifically, Part-based Deep Hashing (PDH) is proposed, in which batches of triplet samples are employed as the input of the deep hashing architecture. Each triplet sample contains two pedestrian images (or parts) with the same identity and one pedestrian image (or part) of the different identity. A triplet loss function is employed with a constraint that the Hamming distance of pedestrian images (or parts) with the same identity is smaller than ones with the different identity. In the experiment, we show that the proposed PDH method yields very competitive re-id accuracy on the large-scale Market-1501 and Market-1501+500K datasets.

Connected through hyperedge HE-1 (induced by source paper "cdpm convolutional deformable part models for semantically aligned person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep representation learning with part loss for person re identification"
    - "video person re identification by temporal residual learning"
    - "person re identification with metric learning using privileged information"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "learning view specific deep networks for person re identification"

Connected through hyperedge HE-2 (induced by source paper "abd net attentive but diverse person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep representation learning with part loss for person re identification"
    - "feature mask network for person re identification"
    - "attribute aware attention model for fine grained representation learning"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "triplet based deep similarity learning for person re identification"

Connected through hyperedge HE-3 (induced by source paper "deep semantic multimodal hashing network for scalable multimedia retrieval"):
  with members:
    - "deep ordinal hashing with spatial attention"
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "deep supervised discrete hashing"
    - "mask r cnn"
    - "object detection from video tubelets with convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "deep cross modal hashing"

Connected through hyperedge HE-4 (induced by source paper "person re identification by semantic region representation and topology constraint"):
  with members:
    - "harmonious attention network for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "camera style adaptation for person re identification"
    - "deep hybrid similarity learning for person re identification"
    - "pose driven deep convolutional model for person re identification"
    - "pose invariant embedding for deep person re identification"

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"
    - "vehicle re identification an efficient baseline using triplet embedding"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "group re identification via transferred single and couple representation learning"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "camera style adaptation for person re identification"
    - "person re identification by deep joint learning of multi loss classification"
    - "group re identification via unsupervised transfer of sparse features encoding"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "pose driven deep convolutional model for person re identification"
    - "pose invariant embedding for deep person re identification"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-7 (induced by source paper "human semantic parsing for person re identification"):
  with members:
    - "scalable person re identification on supervised smoothed manifold"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "person re identification by deep joint learning of multi loss classification"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "pose driven deep convolutional model for person re identification"

Connected through hyperedge HE-8 (induced by source paper "directional statistics based deep metric learning for image classification and retrieval"):
  with members:
    - "triplet center loss for multi view 3d object retrieval"
    - "sphereface deep hypersphere embedding for face recognition"
    - "prototypical networks for few shot learning"
    - "von mises fisher mixture model based deep learning application to face verification"
    - "rethinking feature discrimination and polymerization for large scale recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "fast training of triplet based deep binary embedding networks"
    - "deep metric learning via facility location"

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)