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 detection"
  Abstract: As we move towards large-scale object detection, it is unrealistic to expect annotated training data, in the form of bounding box annotations around objects, for all object classes at sufficient scale, and so methods capable of unseen object detection are required. We propose a novel zero-shot method based on training an end-to-end model that fuses semantic attribute prediction with visual features to propose object bounding boxes for seen and unseen classes. While we utilize semantic features during training, our method is agnostic to semantic information for unseen classes at test-time. Our method retains the efficiency and effectiveness of YOLOv2 redmon2016yolo9000 for objects seen during training, while improving its performance for novel and unseen objects. The ability of state-of-art detection methods to learn discriminative object features to reject background proposals also limits their performance for unseen objects. We posit that, to detect unseen objects, we must incorporate semantic information into the visual domain so that the learned visual features reflect this information and leads to improved recall rates for unseen objects. We test our method on PASCAL VOC and MS COCO dataset and observed significant improvements on the average precision of unseen classes.

Connected through hyperedge HE-1 (induced by source paper "incremental few shot object detection"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "boosting few shot visual learning with self supervision"
    - "large scale long tailed recognition in an open world"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "a closer look at few shot classification"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "objects as points"
    - "image deformation meta networks for one shot learning"

Connected through hyperedge HE-2 (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"
    - "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"
    - "polarity loss for zero shot object detection"

Connected through hyperedge HE-3 (induced by source paper "zstad zero shot temporal activity detection"):
  with members:
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "towards universal representation for unseen action recognition"
    - "zero shot object detection by hybrid region embedding"
    - "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-4 (induced by source paper "few example object detection with model communication"):
  with members:
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "active self paced learning for cost effective and progressive face identification"
    - "collaborative learning for weakly supervised object detection"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "learning to segment human by watching youtube"
    - "multiple instance detection network with online instance classifier refinement"

Connected through hyperedge HE-5 (induced by source paper "zero shot semantic segmentation"):
  with members:
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "zero shot object detection by hybrid region embedding"
    - "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 "coordinated joint multimodal embeddings for generalized audio visual zeroshot classification and retrieval of videos"):
  with members:
    - "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"
    - "audio visual scene analysis with self supervised multisensory features"

Connected through hyperedge HE-7 (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 object detection learning to simultaneously recognize and localize novel concepts"
    - "generative dual adversarial network for generalized zero shot learning"
    - "zero shot object detection by hybrid region embedding"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "context aware zero shot recognition"):
  with members:
    - "classifier and exemplar synthesis for zero shot learning"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "iterative visual reasoning beyond convolutions"
    - "zero shot object detection by hybrid region embedding"
    - "graph r cnn for scene graph generation"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "dock detecting objects by transferring common sense knowledge"

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)