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 "scale coding bag of deep features for human attribute and action recognition"):
  Abstract: Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art.

Includes members:
  Member "scene recognition with cnns objects scales and dataset bias":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "indoor scene recognition in 3d")
  Member "bilinear cnn models for fine grained visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "fuzzy human motion analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "i safe instant suspicious activity identification at the edge using fuzzy decision making")
  Member "multi scale recognition with dag cnns":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convolutional networks with dense connectivity")
  Member "an expressive deep model for human action parsing from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "human pose estimation with spatial contextual information")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "return of the devil in the details delving deep into convolutional nets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on deep hashing methods")
  Member "r cnns for pose estimation and action detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mpm joint representation of motion and position map for cell tracking")

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)