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: "teaching and compressing for low vc dimension"
  Abstract: In this work we study the quantitative relation between VC-dimension and two other basic parameters related to learning and teaching. We present relatively ecient

Connected through hyperedge HE-1 (induced by source paper "optimal collusion free teaching"):
  with members:
    - "unlabeled sample compression schemes and corner peelings for ample and maximum classes"
    - "an overview of machine teaching"
    - "quadratic upper bound for recursive teaching dimension of finite vc classes"
    - "preference based teaching"
    - "sample compression schemes for vc classes"

Connected through hyperedge HE-2 (induced by source paper "labeled compression schemes for extremal classes"):
  with members:
    - "sample compression schemes for vc classes"
    - "bounding embeddings of vc classes into maximum classes"
    - "optimal learners for multiclass problems"
    - "shattering graph orientations and connectivity"
    - "a geometric approach to sample compression"

Connected through hyperedge HE-3 (induced by source paper "hitting set for hypergraphs of low vc dimension"):
  with members:
    - "identifying codes in hereditary classes of graphs and vc dimension"
    - "domination when the stars are out"
    - "solving dominating set in larger classes of graphs fpt algorithms and polynomial kernels"
    - "improved approximation algorithms for geometric set cover"

Connected through hyperedge HE-4 (induced by source paper "sample compression schemes for vc classes"):
  with members:
    - "the optimal sample complexity of pac learning"
    - "optimal learners for multiclass problems"
    - "a geometric approach to sample compression"
    - "simple strategies for large zero sum games with applications to complexity theory"

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.LG(Machine Learning)