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: "the devil is in the middle exploiting mid level representations for cross domain instance matching"
  Abstract: Many vision problems require matching images of object instances across different domains. These include fine-grained sketch-based image retrieval (FG-SBIR) and Person Re-identification (person ReID). Existing approaches attempt to learn a joint embedding space where images from different domains can be directly compared. In most cases, this space is defined by the output of the final layer of a deep neural network (DNN), which primarily contains features of a high semantic level. In this paper, we argue that both high and mid-level features are relevant for cross-domain instance matching (CDIM). Importantly, mid-level features already exist in earlier layers of the DNN. They just need to be extracted, represented, and fused properly with the final layer. Based on this simple but powerful idea, we propose a unified framework for CDIM. Instantiating our framework for FG-SBIR and ReID, we show that our simple models can easily beat the state-of-the-art models, which are often equipped with much more elaborate architectures.

Connected through hyperedge HE-1 (induced by source paper "omni scale feature learning for person re identification"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "attention a big surprise for cross domain person re identification"):
  with members:
    - "eanet enhancing alignment for cross domain person re identification"
    - "towards good practices on building effective cnn baseline model 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"
    - "re identification with consistent attentive siamese networks"
    - "attention aware compositional network for person re identification"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"

Connected through hyperedge HE-3 (induced by source paper "torchreid a library for deep learning person re identification in pytorch"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "multi person articulated tracking with spatial and temporal embeddings"):
  with members:
    - "pose flow efficient online pose tracking"
    - "joint flow temporal flow fields for multi person tracking"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "simple baselines for human pose estimation and tracking"
    - "recurrent pixel embedding for instance grouping"
    - "generative partition networks for multi person pose estimation"
    - "towards accurate multi person pose estimation in the wild"
    - "posetrack a benchmark for human pose estimation and tracking"

Connected through hyperedge HE-5 (induced by source paper "cityflow a city scale benchmark for multi target multi camera vehicle tracking and re identification"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "moana an online learned adaptive appearance model for robust multiple object tracking in 3d"
    - "multi level factorisation net for person re identification"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "harmonious attention network for person re identification"
    - "features for multi target multi camera tracking and re identification"
    - "yolov3 an incremental improvement"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-6 (induced by source paper "hpiln a feature learning framework for cross modality person re identification"):
  with members:
    - "learning discriminative features with multiple granularities for person re identification"
    - "person re identification by camera correlation aware feature augmentation"
    - "batch dropblock network for person re identification and beyond"
    - "harmonious attention network for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "robust depth based person re identification"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"

Connected through hyperedge HE-7 (induced by source paper "a novel teacher student learning framework for occluded person re identification"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "multi level factorisation net for person re identification"
    - "distractor aware siamese networks for visual object tracking"
    - "occluded person re identification"
    - "human semantic parsing for person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"

Connected through hyperedge HE-8 (induced by source paper "ca3net contextual attentional attribute appearance network for person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "person re identification by camera correlation aware feature augmentation"
    - "harmonious attention network for person re identification"
    - "video based person re identification with accumulative motion context"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "re ranking person re identification with k reciprocal encoding"
    - "glad global local alignment descriptor for pedestrian retrieval"

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)