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: "knowledge concentration learning 100k object classifiers in a single cnn"
  Abstract: Fine-grained image labels are desirable for many computer vision applications, such as visual search or mobile AI assistant. These applications rely on image classification models that can produce hundreds of thousands (e.g. 100K) of diversified fine-grained image labels on input images. However, training a network at this vocabulary scale is challenging, and suffers from intolerable large model size and slow training speed, which leads to unsatisfying classification performance. A straightforward solution would be training separate expert networks (specialists), with each specialist focusing on learning one specific vertical (e.g. cars, birds...). However, deploying dozens of expert networks in a practical system would significantly increase system complexity and inference latency, and consumes large amounts of computational resources. To address these challenges, we propose a Knowledge Concentration method, which effectively transfers the knowledge from dozens of specialists (multiple teacher networks) into one single model (one student network) to classify 100K object categories. There are three salient aspects in our method: (1) a multi-teacher single-student knowledge distillation framework; (2) a self-paced learning mechanism to allow the student to learn from different teachers at various paces; (3) structurally connected layers to expand the student network capacity with limited extra parameters. We validate our method on OpenImage and a newly collected dataset, Entity-Foto-Tree (EFT), with 100K categories, and show that the proposed model performs significantly better than the baseline generalist model.

Connected through hyperedge HE-1 (induced by source paper "trained model fusion for object detection using gating network"):
  with members:
    - "adaptive weighting multi field of view cnn for semantic segmentation in pathology"
    - "yolov3 an incremental improvement"
    - "visual saliency prediction using a mixture of deep neural networks"
    - "partial adversarial domain adaptation"
    - "edf ensemble distill and fuse for easy video labeling"
    - "robust deep multi modal learning based on gated information fusion network"
    - "rank of experts detection network ensemble"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "unifying specialist image embedding into universal image embedding"):
  with members:
    - "learning metrics from teachers compact networks for image embedding"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "relational knowledge distillation"
    - "unifying heterogeneous classifiers with distillation"
    - "learning deep representations with probabilistic knowledge transfer"
    - "no fuss distance metric learning using proxies"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-3 (induced by source paper "student becoming the master knowledge amalgamation for joint scene parsing depth estimation and more"):
  with members:
    - "amalgamating knowledge towards comprehensive classification"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "an adversarial regularisation for semi supervised training of structured output neural networks"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "semi and weakly supervised semantic segmentation using generative adversarial network"
    - "monocular depth estimation with hierarchical fusion of dilated cnns and soft weighted sum inference"
    - "joint estimation of camera pose depth deblurring and super resolution from a blurred image sequence"
    - "mask r cnn"

Connected through hyperedge HE-4 (induced by source paper "learning metrics from teachers compact networks for image embedding"):
  with members:
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "metric learning for novelty and anomaly detection"
    - "learning deep representations with probabilistic knowledge transfer"
    - "deep mutual learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "deep image retrieval learning global representations for image search"

Connected through hyperedge HE-5 (induced by source paper "amalgamating filtered knowledge learning task customized student from multi task teachers"):
  with members:
    - "amalgamating knowledge towards comprehensive classification"
    - "fisheyerecnet a multi context collaborative deep network for fisheye image rectification"
    - "deep label distribution learning with label ambiguity"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "hcp a flexible cnn framework for multi label image classification"
    - "cross stitch networks for multi task learning"
    - "identity mappings in deep residual networks"
    - "cnn rnn a unified framework for multi label image classification"

Connected through hyperedge HE-6 (induced by source paper "improving multi person pose estimation using label correction"):
  with members:
    - "joint optimization framework for learning with noisy labels"
    - "data distillation towards omni supervised learning"
    - "learning deep networks from noisy labels with dropout regularization"
    - "cascaded pyramid network for multi person pose estimation"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "feature pyramid networks for object detection"
    - "associative embedding end to end learning for joint detection and grouping"

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)