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: "abd net attentive but diverse person re identification"
  Abstract: Attention mechanism has been shown to be effective for person re-identification (Re-ID). However, the learned attentive feature embeddings which are often not naturally diverse nor uncorrelated, will compromise the retrieval performance based on the Euclidean distance. We advocate that enforcing diversity could greatly complement the power of attention. To this end, we propose an Attentive but Diverse Network (ABD-Net), which seamlessly integrates attention modules and diversity regularization throughout the entire network, to learn features that are representative, robust, and more discriminative. Specifically, we introduce a pair of complementary attention modules, focusing on channel aggregation and position awareness, respectively. Furthermore, a new efficient form of orthogonality constraint is derived to enforce orthogonality on both hidden activations and weights. Through careful ablation studies, we verify that the proposed attentive and diverse terms each contributes to the performance gains of ABD-Net. On three popular benchmarks, ABD-Net consistently outperforms existing state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning"):
  with members:
    - "deep learning for person re identification a survey and outlook"
    - "rethinking person re identification with confidence"
    - "res2net a new multi scale backbone architecture"
    - "omni scale feature learning for person re identification"
    - "attention a big surprise for cross domain person re identification"
    - "searching for a robust neural architecture in four gpu hours"
    - "spherereid deep hypersphere manifold embedding for person re identification"
    - "unsupervised person re identification by soft multilabel learning"

Connected through hyperedge HE-2 (induced by source paper "towards precise intra camera supervised person re identification"):
  with members:
    - "adaptive exploration for unsupervised person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "intra camera supervised person re identification a new benchmark"
    - "adversarial camera alignment network for unsupervised cross camera person re identification"
    - "progressive cross camera soft label learning for semi supervised person re identification"
    - "densely semantically aligned person re identification"

Connected through hyperedge HE-3 (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"
    - "interaction and aggregation network for person re identification"
    - "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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "attention network robustification for person reid"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "relation aware global attention"
    - "spherereid deep hypersphere manifold embedding for person re identification"
    - "auto reid searching for a part aware convnet for person re identification"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "learning discriminative features with multiple granularities for person re identification"

Connected through hyperedge HE-5 (induced by source paper "calibrated domain invariant learning for highly generalizable large scale re identification"):
  with members:
    - "privacy preserving deep visual recognition an adversarial learning framework and a new dataset"
    - "cross domain knowledge learning with dual branch adversarial network for vehicle re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "ram a region aware deep model for vehicle re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "parameter free spatial attention network for person re identification"
    - "focal loss for dense object detection"

Connected through hyperedge HE-6 (induced by source paper "viewpoint aware loss with angular regularization for person re identification"):
  with members:
    - "bag of tricks and a strong baseline for deep person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "dissecting person re identification from the viewpoint of viewpoint"
    - "arcface additive angular margin loss for deep face recognition"
    - "horizontal pyramid matching for person re identification"
    - "pyramidal person re identification via multi loss dynamic training"
    - "human semantic parsing for person re identification"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-7 (induced by source paper "learning to optimize in swarms"):
  with members:
    - "plug and play methods provably converge with properly trained denoisers"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "learned optimizers that scale and generalize"
    - "learning to learn by gradient descent by gradient descent"
    - "learning to optimize"
    - "neural architecture search with reinforcement learning"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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)