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 "putting visual object recognition in context"):
  Abstract: Context plays an important role in visual recognition. Recent studies have shown that visual recognition networks can be fooled by placing objects in inconsistent contexts (e.g. a cow in the ocean). To understand and model the role of contextual information in visual recognition, we systematically and quantitatively investigated ten critical properties of where, when, and how context modulates recognition including amount of context, context and object resolution, geometrical structure of context, context congruence, time required to incorporate contextual information, and temporal dynamics of contextual modulation. The tasks involve recognizing a target object surrounded with context in a natural image. As an essential benchmark, we first describe a series of psychophysics experiments, where we alter one aspect of context at a time, and quantify human recognition accuracy. To computationally assess performance on the same tasks, we propose a biologically inspired context aware object recognition model consisting of a two-stream architecture. The model processes visual information at the fovea and periphery in parallel, dynamically incorporates both object and contextual information, and sequentially reasons about the class label for the target object. Across a wide range of behavioral tasks, the model approximates human level performance without retraining for each task, captures the dependence of context enhancement on image properties, and provides initial steps towards integrating scene and object information for visual recognition.

Includes members:
  Member "approximating cnns with bag of local features models works surprisingly well on imagenet":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning representations by predicting bags of visual words")
  Member "lift the flap context reasoning using object centered graphs":
    also connected through other hyperedges:
      (none)
  Member "iterative visual reasoning beyond convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive graph convolutional network with attention graph clustering for co saliency detection")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "modeling visual context is key to augmenting object detection datasets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mixup regularization for region proposal based object detectors")
  Member "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on feature normalization and data augmentation")
  Member "learning what and where to attend":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stochastic region pooling make attention more expressive")
  Member "learning scene gist with convolutional neural networks to improve object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lift the flap what where and when for context reasoning")

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)