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: "zero shot object detection by hybrid region embedding"
  Abstract: Object detection is considered as one of the most challenging problems in computer vision, since it requires correct prediction of both classes and locations of objects in images. In this study, we define a more difficult scenario, namely zero-shot object detection (ZSD) where no visual training data is available for some of the target object classes. We present a novel approach to tackle this ZSD problem, where a convex combination of embeddings are used in conjunction with a detection framework. For evaluation of ZSD methods, we propose a simple dataset constructed from Fashion-MNIST images and also a custom zero-shot split for the Pascal VOC detection challenge. The experimental results suggest that our method yields promising results for ZSD.

Connected through hyperedge HE-1 (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"
    - "polarity loss for zero shot object detection"

Connected through hyperedge HE-2 (induced by source paper "zstad zero shot temporal activity detection"):
  with members:
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "towards universal representation for unseen action recognition"
    - "w talc weakly supervised temporal activity localization and classification"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "similarity r c3d for few shot temporal activity detection"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-3 (induced by source paper "gtnet generative transfer network for zero shot object detection"):
  with members:
    - "out of distribution detection for generalized zero shot action recognition"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "generative dual adversarial network for generalized zero shot learning"
    - "polarity loss for zero shot object detection"
    - "visual data synthesis via gan for zero shot video classification"
    - "semantic autoencoder for zero shot learning"
    - "feature generating networks for zero shot learning"
    - "learning a deep embedding model for zero shot learning"

Connected through hyperedge HE-4 (induced by source paper "image captioning with unseen objects"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "neural baby talk"
    - "polarity loss for zero shot object detection"
    - "transductive unbiased embedding for zero shot learning"
    - "decoupled novel object captioner"
    - "semantic autoencoder for zero shot learning"
    - "focal loss for dense object detection"

Connected through hyperedge HE-5 (induced by source paper "zero shot semantic segmentation"):
  with members:
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "transductive unbiased embedding for zero shot learning"
    - "context encoding for semantic segmentation"
    - "semantic autoencoder for zero shot learning"
    - "a simple exponential family framework for zero shot learning"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"

Connected through hyperedge HE-6 (induced by source paper "detecting human object interactions via functional generalization"):
  with members:
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "interact as you intend intention driven human object interaction detection"
    - "detecting unseen visual relations using analogies"
    - "women also snowboard overcoming bias in captioning models"
    - "ican instance centric attention network for human object interaction detection"
    - "no frills human object interaction detection factorization layout encodings and training techniques"
    - "detecting visual relationships using box attention"
    - "pairwise body part attention for recognizing human object interactions"

Connected through hyperedge HE-7 (induced by source paper "comparison network for one shot conditional object detection"):
  with members:
    - "meta learning with latent embedding optimization"
    - "metalearning with hebbian fast weights"
    - "large margin few shot learning"
    - "focal loss for dense object detection"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "weakly supervised object localization with multi fold multiple instance learning"

Connected through hyperedge HE-8 (induced by source paper "dont even look once synthesizing features for zero shot detection"):
  with members:
    - "probabilistic semantic retrieval for surveillance videos with activity graphs"
    - "transferable contrastive network for generalized zero shot learning"
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "generative dual adversarial network for generalized zero shot learning"
    - "generalized zero shot recognition based on visually semantic embedding"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero 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)