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 "detangling people individuating multiple close people and their body parts via region assembly"):
  Abstract: Today's person detection methods work best when people are in common upright poses and appear reasonably well spaced out in the image. However, in many real images, that's not what people do. People often appear quite close to each other, e.g., with limbs linked or heads touching, and their poses are often not pedestrian-like. We propose an approach to detangle people in multi-person images. We formulate the task as a region assembly problem. Starting from a large set of overlapping regions from body part semantic segmentation and generic object proposals, our optimization approach reassembles those pieces together into multiple person instances. It enforces that the composed body part regions of each person instance obey constraints on relative sizes, mutual spatial relationships, foreground coverage, and exclusive label assignments when overlapping. Since optimal region assembly is a challenging combinatorial problem, we present a Lagrangian relaxation method to accelerate the lower bound estimation, thereby enabling a fast branch and bound solution for the global optimum. As output, our method produces a pixel-level map indicating both 1) the body part labels (arm, leg, torso, and head), and 2) which parts belong to which individual person. Our results on three challenging datasets show our method is robust to clutter, occlusion, and complex poses. It outperforms a variety of competing methods, including existing detector CRF methods and region CNN approaches. In addition, we demonstrate its impact on a proxemics recognition task, which demands a precise representation of "whose body part is where" in crowded images.

Includes members:
  Member "deepercut a deeper stronger and faster multi person pose estimation model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "higherhrnet scale aware representation learning for bottom up human pose estimation")
  Member "joint object and part segmentation using deep learned potentials":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning compositional neural information fusion for human parsing")
  Member "deepcut joint subset partition and labeling for multi person pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "combining local appearance and holistic view dual source deep neural networks for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "from kinematics to dynamics estimating center of pressure and base of support from video frames of human motion")
  Member "hypercolumns for object segmentation and fine grained localization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convolutional networks with dense connectivity")
  Member "simultaneous detection and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep affinity net instance segmentation via affinity")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "articulated pose estimation by a graphical model with image dependent pairwise relations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")

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)