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: "algorithmic performance accuracy trade off in 3d vision applications using hypermapper"
  Abstract: In this paper we investigate an emerging application, 3D scene understanding, likely to be significant in the mobile space in the near future. The goal of this exploration is to reduce execution time while meeting our quality of result objectives. In previous work we showed for the first time that it is possible to map this application to power constrained embedded systems, highlighting that decision choices made at the algorithmic design-level have the most impact. #R##N#As the algorithmic design space is too large to be exhaustively evaluated, we use a previously introduced multi-objective Random Forest Active Learning prediction framework dubbed HyperMapper, to find good algorithmic designs. We show that HyperMapper generalizes on a recent cutting edge 3D scene understanding algorithm and on a modern GPU-based computer architecture. HyperMapper is able to beat an expert human hand-tuning the algorithmic parameters of the class of Computer Vision applications taken under consideration in this paper automatically. In addition, we use crowd-sourcing using a 3D scene understanding Android app to show that the Pareto front obtained on an embedded system can be used to accelerate the same application on all the 83 smart-phones and tablets crowd-sourced with speedups ranging from 2 to over 12.

Connected through hyperedge HE-1 (induced by source paper "polystore accelerated polystore system for heterogeneous workloads"):
  with members:
    - "relay a new ir for machine learning frameworks"
    - "rheemix in the data jungle a cross platform query optimizer"
    - "in rdbms hardware acceleration of advanced analytics"
    - "practical design space exploration"
    - "in datacenter performance analysis of a tensor processing unit"
    - "empowering in memory relational database engines with native graph processing"
    - "pipegen data pipe generator for hybrid analytics"
    - "the bigdawg polystore system and architecture"

Connected through hyperedge HE-2 (induced by source paper "futuremapping the computational structure of spatial ai systems"):
  with members:
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "co fusion real time segmentation tracking and fusion of multiple objects"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "unsupervised learning of depth and ego motion from video"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "da rnn semantic mapping with data associated recurrent neural networks"
    - "a berkeley view of systems challenges for ai"

Connected through hyperedge HE-3 (induced by source paper "slambooster an application aware controller for approximation in slam"):
  with members:
    - "cnn slam real time dense monocular slam with learned depth prediction"
    - "introducing slambench a performance and accuracy benchmarking methodology for slam"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-4 (induced by source paper "practical design space exploration"):
  with members:
    - "a model driven approach for a new generation of adaptive libraries"

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.CV(Computer Vision and Pattern Recognition)