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: "interaction and aggregation network for person re identification"
  Abstract: Person re-identification (reID) benefits greatly from deep convolutional neural networks (CNNs) which learn robust feature embeddings. However, CNNs are inherently limited in modeling the large variations in person pose and scale due to their fixed geometric structures. In this paper, we propose a novel network structure, Interaction-and-Aggregation (IA), to enhance the feature representation capability of CNNs. Firstly, Spatial IA (SIA) module is introduced. It models the interdependencies between spatial features and then aggregates the correlated features corresponding to the same body parts. Unlike CNNs which extract features from fixed rectangle regions, SIA can adaptively determine the receptive fields according to the input person pose and scale. Secondly, we introduce Channel IA (CIA) module which selectively aggregates channel features to enhance the feature representation, especially for smallscale visual cues. Further, IA network can be constructed by inserting IA blocks into CNNs at any depth. We validate the effectiveness of our model for person reID by demonstrating its superiority over state-of-the-art methods on three benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond"):
  with members:
    - "recover and identify a generative dual model for cross resolution person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "improving person re identification by attribute and identity learning"
    - "cgans with projection discriminator"
    - "multi level factorisation net for person re identification"
    - "learning to adapt structured output space for semantic segmentation"
    - "harmonious attention network for person re identification"

Connected through hyperedge HE-2 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification"):
  with members:
    - "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"
    - "learning discriminative features with multiple granularities for person re identification"

Connected through hyperedge HE-3 (induced by source paper "cross modality person re identification with shared specific feature transfer"):
  with members:
    - "hpiln a feature learning framework for cross modality person re identification"
    - "learning disentangled representation for robust person re identification"
    - "a new benchmark and approach for fine grained cross media retrieval"
    - "joint discriminative and generative learning for person re identification"
    - "beyond intra modality discrepancy a comprehensive survey of heterogeneous person re identification"
    - "mtfh a matrix tri factorization hashing framework for efficient cross modal retrieval"
    - "attend to the difference cross modality person re identification via contrastive correlation"
    - "improving description based person re identification by multi granularity image text alignments"

Connected through hyperedge HE-4 (induced by source paper "deep representation learning on long tailed data a learnable embedding augmentation perspective"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "class balanced loss based on effective number of samples"
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "arcface additive angular margin loss for deep face recognition"
    - "gradient harmonized single stage detector"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-5 (induced by source paper "omni scale feature learning for person re identification"):
  with members:
    - "aanet attribute attention network for person re identifications"
    - "joint discriminative and generative learning for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "torchreid a library for deep learning person re identification in pytorch"
    - "auto reid searching for a part aware convnet for person re identification"
    - "learning generalisable omni scale representations for person re identification"
    - "on the convergence of adam and beyond"
    - "invariance matters exemplar memory for domain adaptive person re identification"

Connected through hyperedge HE-6 (induced by source paper "torchreid a library for deep learning person re identification in pytorch"):
  with members:
    - "omni scale feature learning for person re identification"
    - "on the variance of the adaptive learning rate and beyond"
    - "torchmeta a meta learning library for pytorch"
    - "simpledet a simple and versatile distributed framework for object detection and instance recognition"
    - "learning generalisable omni scale representations for person re identification"
    - "huggingface s transformers state of the art natural language processing"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "on the convergence of adam and beyond"

Connected through hyperedge HE-7 (induced by source paper "adaptive graph representation learning for video person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "learning context graph for person search"
    - "omni scale feature learning for person re identification"
    - "video person re identification by temporal residual learning"
    - "scan self and collaborative attention network for video person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "spatially and temporally efficient non local attention network for video based person re identification"

Connected through hyperedge HE-8 (induced by source paper "unified multifaceted feature learning for person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a dual path model with adaptive attention for vehicle re identification"
    - "joint discriminative and generative learning for person re identification"
    - "deep representation learning with part loss for person re identification"
    - "abd net attentive but diverse person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"

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)