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 "hi fi hierarchical feature integration for skeleton detection"):
  Abstract: In natural images, skeleton scales (thickness) may significantly vary among objects and object parts. Thus, robust skeleton detection requires more powerful multi-scale feature integration ability than other vision tasks. In this paper, we present a new convolutional neural network (CNN) architecture by introducing a novel hierarchical feature integration mechanism, named Hi-Fi, to address the object skeleton detection problem. CNNs can capture high-level semantics from deeper layers as well as low-level details from shallower layers. #R##N#By hierarchically integrating convolutional features with bi-direction guidance, our approach (1) enables mutual refinement across features of different levels, and (2) possesses strong ability to capture both rich object context and high-resolution details. Experimental results on several benchmarks show that our method significantly outperforms the state-of-the-art methods in terms of effectively fusing features from very different scales, as evidenced by a considerable performance improvement on skeleton detection. Our method also refreshes the state-of-the-art performance of boundary detection task.

Includes members:
  Member "deeply supervised salient object detection with short connections":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "amat medial axis transform for natural images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predicting animation skeletons for 3d articulated models via volumetric nets")
  Member "richer convolutional features for edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "srn side output residual network for object symmetry detection in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "min entropy latent model for weakly supervised object detection")
  Member "deepskeleton learning multi task scale associated deep side outputs for object skeleton extraction in natural images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "predicting animation skeletons for 3d articulated models via volumetric nets")
  Member "amulet aggregating multi level convolutional features for salient object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "object contour detection with a fully convolutional encoder decoder network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")
  Member "holistically nested edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")

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)