human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "real time error control for surgical simulation"
  Abstract: Objective : To present the first a posteriori error-driven adaptive finite element approach for real-time simulation, and to demonstrate the method on a needle insertion problem.  Methods : We use corotational elasticity and a frictional needle/tissue interaction model. The problem is solved using finite elements within SOFA. 1  For simulating soft tissue deformation, the refinement strategy relies upon a hexahedron-based finite element method, combined with a posteriori error estimation driven local   $h$  -refinement.  Results : We control the local and global error level in the mechanical fields (e.g., displacement or stresses) during the simulation. We show the convergence of the algorithm on academic examples, and demonstrate its practical usability on a percutaneous procedure involving needle insertion in a liver. For the latter case, we compare the force–displacement curves obtained from the proposed adaptive algorithm with that obtained from a uniform refinement approach.  Conclusions : Error control guarantees that a tolerable error level is not exceeded during the simulations. Local mesh refinement accelerates simulations.  Significance : Our work provides a first step to discriminate between discretization error and modeling error by providing a robust quantification of discretization error during simulations.

Connected through hyperedge HE-1 (induced by source paper "corotational cut finite element method for real time surgical simulation application to needle insertion simulation"):
  with members:
    - "controlling the error on target motion through real time mesh adaptation applications to deep brain stimulation"

Connected through hyperedge HE-2 (induced by source paper "deformable models for surgical simulation a survey"):
  with members:
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "suite of finite element algorithms for accurate computation of soft tissue deformation for surgical simulation"

Connected through hyperedge HE-3 (induced by source paper "quantifying discretization errors for soft tissue simulation in computer assisted surgery a preliminary study"):
  with members:
    - "suite of finite element algorithms for accurate computation of soft tissue deformation for surgical simulation"

Question: Please tell me which class the center node 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.NA(Numerical Analysis)