human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "any shot object detection"):
  Abstract: Previous work on novel object detection considers zero or few-shot settings where none or few examples of each category are available for training. In real world scenarios, it is less practical to expect that 'all' the novel classes are either unseen or {have} few-examples. Here, we propose a more realistic setting termed 'Any-shot detection', where totally unseen and few-shot categories can simultaneously co-occur during inference. Any-shot detection offers unique challenges compared to conventional novel object detection such as, a high imbalance between unseen, few-shot and seen object classes, susceptibility to forget base-training while learning novel classes and distinguishing novel classes from the background. To address these challenges, we propose a unified any-shot detection model, that can concurrently learn to detect both zero-shot and few-shot object classes. Our core idea is to use class semantics as prototypes for object detection, a formulation that naturally minimizes knowledge forgetting and mitigates the class-imbalance in the label space. Besides, we propose a rebalanced loss function that emphasizes difficult few-shot cases but avoids overfitting on the novel classes to allow detection of totally unseen classes. Without bells and whistles, our framework can also be used solely for Zero-shot detection and Few-shot detection tasks. We report extensive experiments on Pascal VOC and MS-COCO datasets where our approach is shown to provide significant improvements.

Includes members:
  Member "anchor loss modulating loss scale based on prediction difficulty":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "toward fast and accurate human pose estimation via soft gated skip connections")
  Member "f vaegan d2 a feature generating framework for any shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to compare relation semantic alignment for few shot learning")
  Member "few example object detection with model communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "zero shot detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "incremental few shot object detection")
  Member "a closer look at few shot classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "revisiting meta learning as supervised learning")
  Member "domain invariant projection learning for zero shot recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "meta learning across meta tasks for few shot learning")
  Member "zero shot object detection learning to simultaneously recognize and localize novel concepts":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "incremental few shot object detection")
  Member "zero shot object detection by hybrid region embedding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "zstad zero shot temporal activity detection")

Question: Please tell me which class the source paper 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)