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 "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition"):
  Abstract: In this paper, we build on a deep translational action recognition network which takes RGB frames as input to learn to predict both action concepts and auxiliary supervisory feature descriptors e.g., Optical Flow Features and/or Improved Dense Trajectory descriptors. The translation is performed by so-called hallucination streams trained to predict auxiliary cues which are simultaneously fed into classification layers, and then hallucinated for free at the testing stage to boost recognition. #R##N#In this paper, we design and hallucinate two descriptors, one leveraging four popular object detectors applied to training videos, and the other leveraging image- and video-level saliency detectors. The first descriptor encodes the detector- and ImageNet-wise class prediction scores, confidence scores, and spatial locations of bounding boxes and frame indexes to capture the spatio-temporal distribution of features per video. Another descriptor encodes spatio-angular gradient distributions of saliency maps and intensity patterns. #R##N#Inspired by the characteristic function of the probability distribution, we capture four statistical moments on the above intermediate descriptors. As numbers of coefficients in the mean, covariance, coskewness and cokurtotsis grow linearly, quadratically, cubically and quartically w.r.t. the dimension of feature vectors, we describe the covariance matrix by its leading n' eigenvectors (so-called subspace) and we capture skewness/kurtosis rather than costly coskewness/cokurtosis. We obtain state of the art on three popular datasets.

Includes members:
  Member "hallucinating optical flow features for video classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bypass enhancement rgb stream model for pedestrian action recognition of autonomous vehicles")
  Member "few shot learning via saliency guided hallucination of samples":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "incremental few shot object detection")
  Member "assemblenet searching for multi stream neural connectivity in video architectures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how to train your super net an analysis of training heuristics in weight sharing nas")
  Member "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "predictively encoded graph convolutional network for noise robust skeleton based action recognition")
  Member "deeply supervised salient object detection with short connections":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "learning discriminative video representations using adversarial perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scsampler sampling salient clips from video for efficient action recognition")
  Member "evolving space time neural architectures for videos":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a multigrid method for efficiently training video models")
  Member "a deeper look at power normalizations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "few shot action recognition via improved attention with self supervision")

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)