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: "deep person learning discriminative deep features for person re identification"
  Abstract: Recently, many methods of person re-identification (Re-ID) rely on part-based feature representation to learn a discriminative pedestrian descriptor. However, the spatial context between these parts is ignored for the independent extractor to each separate part. In this paper, we propose to apply Long Short-Term Memory (LSTM) in an end-to-end way to model the pedestrian, seen as a sequence of body parts from head to foot. Integrating the contextual information strengthens the discriminative ability of local representation. We also leverage the complementary information between local and global feature. Furthermore, we integrate both identification task and ranking task in one network, where a discriminative embedding and a similarity measurement are learned concurrently. This results in a novel three-branch framework named Deep-Person, which learns highly discriminative features for person Re-ID. Experimental results demonstrate that Deep-Person outperforms the state-of-the-art methods by a large margin on three challenging datasets including Market-1501, CUHK03, and DukeMTMC-reID. Specifically, combining with a re-ranking approach, we achieve a 90.84% mAP on Market-1501 under single query setting.

Connected through hyperedge HE-1 (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-2 (induced by source paper "semantics aligned representation learning for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "re identification supervised texture generation"
    - "densebody directly regressing dense 3d human pose and shape from a single color image"
    - "improving person re identification by attribute and identity learning"
    - "learning discriminative features with multiple granularities for person re identification"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "densereg fully convolutional dense shape regression in the wild"
    - "attention aware compositional network for person re identification"

Connected through hyperedge HE-3 (induced by source paper "relation aware global attention"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "residual non local attention networks for image restoration"
    - "learning discriminative features with multiple granularities for person re identification"
    - "ocnet object context network for scene parsing"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "horizontal pyramid matching for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "recognizing partial biometric patterns"

Connected through hyperedge HE-4 (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-5 (induced by source paper "progressive learning algorithm for efficient person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "harmonious attention network for person re identification"
    - "pyramid person matching network for person re identification"
    - "re id done right towards good practices for person re identification"
    - "resource aware person re identification across multiple resolutions"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-6 (induced by source paper "relation network for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "horizontal pyramid matching for person re identification"
    - "actor centric relation network"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "object level visual reasoning in videos"

Connected through hyperedge HE-7 (induced by source paper "pedestrian re identification based on tree branch network with local and global learning"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "deep mutual learning"
    - "glad global local alignment descriptor for pedestrian retrieval"

Connected through hyperedge HE-8 (induced by source paper "hetero center loss for cross modality person re identification"):
  with members:
    - "hpiln a feature learning framework for cross modality person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "sampling matters in deep embedding learning"
    - "in defense of the triplet loss for person re identification"
    - "alignedreid surpassing human level performance in person re identification"
    - "cross domain visual matching via generalized similarity measure and feature learning"
    - "transferring a semantic representation for person re identification and search"

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)