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 "object detection can be improved using human derived contextual expectations"):
  Abstract: Each object in the world occurs in a specific context: cars are seen on highways but not in forests. Contextual information is generally thought to facilitate computation by constraining locations to search. But can knowing context yield tangible benefits in object detection? For it to do so, scene context needs to be learned independently from target features. However this is impossible in traditional object detection where classifiers are trained on images containing both target features and surrounding coarse scene features. In contrast, we humans have the opportunity to learn context and target features separately, such as when we see highways without cars. Here we show for the first time that human-derived scene expectations can be used to improve object detection performance in machines. To measure these expectations, we asked human subjects to indicate the scale, location and likelihood at which targets may occur on scenes containing no targets. Humans showed highly systematic expectations that we could accurately predict using scene features. We then augmented state-of-the-art object detectors (based on deep neural networks) with these human-derived expectations on novel scenes to produce a significant (1-3%) improvement in detecting cars and people in scenes. This improvement was due to low-confidence detector matches being correctly relabeled as targets when they occurred in likely scenes.

Includes members:
  Member "seeing what is not there learning context to determine where objects are missing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generating 3d people in scenes without people")
  Member "semantic understanding of scenes through the ade20k dataset":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "object recognition with and without objects":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploring the origins and prevalence of texture bias in convolutional neural networks")
  Member "a large scale car dataset for fine grained categorization and verification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images")
  Member "contextual action recognition with r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gid net detecting human object interaction with global and instance dependency")
  Member "inside outside net detecting objects in context with skip pooling and recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)