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: "topological and statistical behavior classifiers for tracking applications"
  Abstract: We introduce the first unified theory for target tracking using Multiple Hypothesis Tracking, Topological Data Analysis, and machine learning. Our string of innovations are 1) robust topological features are used to encode behavioral information, 2) statistical models are fitted to distributions over these topological features, and 3) the target type classification methods of Wigren and Bar Shalom et al. are employed to exploit the resulting likelihoods for topological features inside of the tracking procedure. To demonstrate the efficacy of our approach, we test our procedure on synthetic vehicular data generated by the Simulation of Urban Mobility package.

Connected through hyperedge HE-1 (induced by source paper "stabilizing the unstable output of persistent homology computations"):
  with members:
    - "supervised learning of labeled pointcloud differences via cover tree entropy reduction"
    - "graph induced complex on point data"
    - "robust statistics hypothesis testing and confidence intervals for persistent homology on metric measure spaces"

Connected through hyperedge HE-2 (induced by source paper "a persistence landscapes toolbox for topological statistics"):
  with members:
    - "persistence images a stable vector representation of persistent homology"
    - "persistence images an alternative persistent homology representation"
    - "introduction to the r package tda"

Connected through hyperedge HE-3 (induced by source paper "geometric cross modal comparison of heterogeneous sensor data"):
  with members:
    - "deep perceptual mapping for cross modal face recognition"
    - "cover song identification with timbral shape sequences"

Connected through hyperedge HE-4 (induced by source paper "persistence images a stable vector representation of persistent homology"):
  with members:
    - "topological descriptors for 3d surface analysis"

Connected through hyperedge HE-5 (induced by source paper "stabilizing the output of persistent homology computations"):
  with members:
    - "graph induced complex on point data"

Connected through hyperedge HE-6 (induced by source paper "multi scale local shape analysis and feature selection in machine learning applications"):
  with members:
    - "dimension detection with local homology"
    - "3d terrestrial lidar data classification of complex natural scenes using a multi scale dimensionality criterion applications in geomorphology"
    - "non linear index coding outperforming the linear optimum"

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.SY(Systems and Control)