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: "lstd a low shot transfer detector for object detection"
  Abstract: Recent advances in object detection are mainly driven by deep learning with large-scale detection benchmarks. However, the fully-annotated training set is often limited for a target detection task, which may deteriorate the performance of deep detectors. To address this challenge, we propose a novel low-shot transfer detector (LSTD) in this paper, where we leverage rich source-domain knowledge to construct an effective target-domain detector with very few training examples. The main contributions are described as follows. First, we design a flexible deep architecture of LSTD to alleviate transfer difficulties in low-shot detection. This architecture can integrate the advantages of both SSD and Faster RCNN in a unified deep framework. Second, we introduce a novel regularized transfer learning framework for low-shot detection, where the transfer knowledge (TK) and background depression (BD) regularizations are proposed to leverage object knowledge respectively from source and target domains, in order to further enhance fine-tuning with a few target images. Finally, we examine our LSTD on a number of challenging low-shot detection experiments, where LSTD outperforms other state-of-the-art approaches. The results demonstrate that LSTD is a preferable deep detector for low-shot scenarios.

Connected through hyperedge HE-1 (induced by source paper "starnet towards weakly supervised few shot detection and explainable few shot classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "dense classification and implanting for few shot learning"
    - "generative modeling for small data object detection"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "finding task relevant features for few shot learning by category traversal"
    - "laso label set operations networks for multi label few shot learning"
    - "learning to find common objects across few image collections"

Connected through hyperedge HE-2 (induced by source paper "os2d one stage one shot object detection by matching anchor features"):
  with members:
    - "ranked list loss for deep metric learning"
    - "one shot object detection with co attention and co excitation"
    - "one shot instance segmentation"
    - "neighbourhood consensus networks"
    - "repmet representative based metric learning for classification and one shot object detection"
    - "end to end representation learning for correlation filter based tracking"
    - "end to end weakly supervised semantic alignment"
    - "non local neural networks"

Connected through hyperedge HE-3 (induced by source paper "any shot object detection"):
  with members:
    - "anchor loss modulating loss scale based on prediction difficulty"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "few example object detection with model communication"
    - "zero shot detection"
    - "a closer look at few shot classification"
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "zero shot object detection by hybrid region embedding"

Connected through hyperedge HE-4 (induced by source paper "progressive object transfer detection"):
  with members:
    - "few example object detection with model communication"
    - "deep self taught learning for weakly supervised object localization"
    - "saliency guided end to end learning for weakly supervised object detection"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "multiple instance detection network with online instance classifier refinement"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "weakly supervised cascaded convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "few shot adaptive faster r cnn"):
  with members:
    - "few example object detection with model communication"
    - "semantic foggy scene understanding with synthetic data"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "focal loss for dense object detection"
    - "few shot adversarial domain adaptation"
    - "when unsupervised domain adaptation meets tensor representations"
    - "cascade r cnn delving into high quality object detection"

Connected through hyperedge HE-6 (induced by source paper "few shot object detection with attention rpn and multi relation detector"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "few example object detection with model communication"
    - "edge labeling graph neural network for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "meta r cnn towards general solver for instance level few shot learning"
    - "one shot segmentation in clutter"

Connected through hyperedge HE-7 (induced by source paper "meta r cnn towards general solver for instance level few shot learning"):
  with members:
    - "provable guarantees for gradient based meta learning"
    - "panoptic feature pyramid networks"
    - "few shot object detection via feature reweighting"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"
    - "detecting and recognizing human object interactions"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"

Connected through hyperedge HE-8 (induced by source paper "meta learning algorithms for few shot computer vision"):
  with members:
    - "few shot adaptive faster r cnn"
    - "a closer look at few shot classification"
    - "yolov3 an incremental improvement"
    - "few shot object detection via feature reweighting"
    - "meta learning with differentiable closed form solvers"
    - "low shot learning from imaginary data"
    - "focal loss for dense object detection"
    - "prototypical networks for few shot learning"

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)