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"
  Abstract: We introduce and tackle the problem of zero-shot object detection (ZSD), which aims to detect object classes which are not observed during training. We work with a challenging set of object classes, not restricting ourselves to similar and/or fine-grained categories cf. prior works on zero-shot classification. We follow a principled approach by first adapting visual-semantic embeddings for ZSD. We then discuss the problems associated with selecting a background class and motivate two background-aware approaches for learning robust detectors. One of these models uses a fixed background class and the other is based on iterative latent assignments. We also outline the challenge associated with using a limited number of training classes and propose a solution based on dense sampling of the semantic label space using auxiliary data with a large number of categories. We propose novel splits of two standard detection datasets - MSCOCO and VisualGenome and discuss extensive empirical results to highlight the benefits of the proposed methods. We provide useful insights into the algorithm and conclude by posing some open questions to encourage further research.

Connected through hyperedge HE-1 (induced by source paper "coordinated joint multimodal embeddings for generalized audio visual zeroshot classification and retrieval of videos"):
  with members:
    - "zero shot detection"
    - "learning to localize sound source in visual scenes"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "identify locate and separate audio visual object extraction in large video collections using weak supervision"
    - "learning to separate object sounds by watching unlabeled video"
    - "the sound of pixels"

Connected through hyperedge HE-2 (induced by source paper "omnia faster r cnn detection in the wild through dataset merging and soft distillation"):
  with members:
    - "zero shot detection"
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "cornernet detecting objects as paired keypoints"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "combining heterogeneously labeled datasets for training segmentation networks"
    - "reducing overconfident errors outside the known distribution"
    - "open set domain adaptation by backpropagation"
    - "a two stream mutual attention network for semi supervised biomedical segmentation with noisy labels"

Connected through hyperedge HE-3 (induced by source paper "few shot object detection via feature reweighting"):
  with members:
    - "few example object detection with model communication"
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "distilling critical paths in convolutional neural networks"
    - "yolov3 an incremental improvement"
    - "revisiting the importance of individual units in cnns via ablation"
    - "low shot learning from imaginary data"
    - "lstd a low shot transfer detector for object detection"

Connected through hyperedge HE-4 (induced by source paper "repmet representative based metric learning for classification and one shot object detection"):
  with members:
    - "few example object detection with model communication"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "deep meta learning learning to learn in the concept space"
    - "directional statistics based deep metric learning for image classification and retrieval"
    - "low shot learning from imaginary data"
    - "learning transferable architectures for scalable image recognition"
    - "deep metric learning with angular loss"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-5 (induced by source paper "zero shot object detection by hybrid region embedding"):
  with members:
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "transductive unbiased embedding for zero shot learning"
    - "semantic autoencoder for zero shot learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "how to transfer zero shot object recognition via hierarchical transfer of semantic attributes"

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)