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 "convex optimisation for inverse kinematics"):
  Abstract: We consider the problem of inverse kinematics (IK), where one wants to find the parameters of a given kinematic skeleton that best explain a set of observed 3D joint locations. The kinematic skeleton has a tree structure, where each node is a joint that has an associated geometric transformation that is propagated to all its child nodes. The IK problem has various applications in vision and graphics, for example for tracking or reconstructing articulated objects, such as human hands or bodies. Most commonly, the IK problem is tackled using local optimisation methods. A major downside of these approaches is that, due to the non-convex nature of the problem, such methods are prone to converge to unwanted local optima and therefore require a good initialisation. In this paper we propose a convex optimisation approach for the IK problem based on semidefinite programming, which admits a polynomial-time algorithm that globally solves (a relaxation of) the IK problem. Experimentally, we demonstrate that the proposed method significantly outperforms local optimisation methods using different real-world skeletons.

Includes members:
  Member "reticam real time human performance capture from monocular video":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning 3d human dynamics from video")
  Member "convex relaxations for pose graph optimization with outliers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graduated non convexity for robust spatial perception from non minimal solvers to global outlier rejection")
  Member "rotation averaging and strong duality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the distribution of minima in intrinsic metric rotation averaging")
  Member "ganerated hands for real time 3d hand tracking from monocular rgb":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "vnect real time 3d human pose estimation with a single rgb camera":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")
  Member "real time hand tracking under occlusion from an egocentric rgb d sensor":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rotation invariant mixed graphical model network for 2d hand pose estimation")
  Member "non iterative rigid 2d 3d point set registration using semidefinite programming":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "linearly converging quasi branch and bound algorithms for global rigid registration")
  Member "near optimal joint object matching via convex relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rgb d individual segmentation")

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.LG(Machine Learning)