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: "batch dropblock network for person re identification and beyond"
  Abstract: Since the person re-identification task often suffers from the problem of pose changes and occlusions, some attentive local features are often suppressed when training CNNs. In this paper, we propose the Batch DropBlock (BDB) Network which is a two branch network composed of a conventional ResNet-50 as the global branch and a feature dropping branch. The global branch encodes the global salient representations. Meanwhile, the feature dropping branch consists of an attentive feature learning module called Batch DropBlock, which randomly drops the same region of all input feature maps in a batch to reinforce the attentive feature learning of local regions. The network then concatenates features from both branches and provides a more comprehensive and spatially distributed feature representation. Albeit simple, our method achieves state-of-the-art on person re-identification and it is also applicable to general metric learning tasks. For instance, we achieve 76.4% Rank-1 accuracy on the CUHK03-Detect dataset and 83.0% Recall-1 score on the Stanford Online Products dataset, outperforming the existing works by a large margin (more than 6%).

Connected through hyperedge HE-1 (induced by source paper "compounding the performance improvements of assembled techniques in a convolutional neural network"):
  with members:
    - "selective kernel networks"
    - "adversarial examples improve image recognition"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "do better imagenet models transfer better"
    - "bag of tricks for image classification with convolutional neural networks"
    - "dropblock a regularization method for convolutional networks"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-2 (induced by source paper "diversity achieving slow dropblock network for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "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"
    - "harmonious attention network for person re identification"
    - "dropblock a regularization method for convolutional networks"
    - "attention aware compositional network for person re identification"
    - "scpnet spatial channel parallelism network for joint holistic and partial person re identification"

Connected through hyperedge HE-3 (induced by source paper "combination of multiple global descriptors for image retrieval"):
  with members:
    - "large scale landmark retrieval recognition under a noisy and diverse dataset"
    - "2nd place and 2nd place solution to kaggle landmark recognition andretrieval competition 2019"
    - "visualizing deep similarity networks"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "learning discriminative features with multiple granularities for person re identification"
    - "classification is a strong baseline for deep metric learning"
    - "harmonious attention network for person re identification"
    - "deep randomized ensembles for metric learning"

Connected through hyperedge HE-4 (induced by source paper "attention deep model with multi scale deep supervision for person re identification"):
  with members:
    - "beyond human parts dual part aligned representations for person re identification"
    - "ranked list loss for deep metric learning"
    - "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"
    - "auto reid searching for a part aware convnet for person re identification"
    - "selective kernel networks"
    - "multi scale body part mask guided attention for person re identification"

Connected through hyperedge HE-5 (induced by source paper "second order non local attention networks for 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"
    - "bag of tricks for image classification with convolutional neural networks"
    - "harmonious attention network for person re identification"
    - "dropblock a regularization method for convolutional networks"
    - "a coarse to fine pyramidal model for person re identification via multi loss dynamic training"
    - "compact generalized non local network"
    - "dual attention matching network for context aware feature sequence based person re identification"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "a strong baseline and batch normalization neck for deep person re identification"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "spherereid deep hypersphere manifold embedding for person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "features for multi target multi camera tracking and re identification"
    - "attention aware compositional network for person re identification"

Connected through hyperedge HE-8 (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"
    - "abd net attentive but diverse person re identification"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "bag of tricks and a strong baseline for deep 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)