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 "labeled compression schemes for extremal classes"):
  Abstract: It is a long-standing open problem whether there always exists a compression scheme whose size is of the order of the Vapnik-Chervonienkis (VC) dimension $d$. Recently compression schemes of size exponential in $d$ have been found for any concept class of VC dimension $d$. Previously, compression schemes of size $d$ have been given for maximum classes, which are special concept classes whose size equals an upper bound due to Sauer-Shelah. We consider a generalization of maximum classes called extremal classes. Their definition is based on a powerful generalization of the Sauer-Shelah bound called the Sandwich Theorem, which has been studied in several areas of combinatorics and computer science. The key result of the paper is a construction of a sample compression scheme for extremal classes of size equal to their VC dimension. We also give a number of open problems concerning the combinatorial structure of extremal classes and the existence of unlabeled compression schemes for them.

Includes members:
  Member "teaching and compressing for low vc dimension":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal collusion free teaching")
  Member "sample compression schemes for vc classes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal collusion free teaching")
  Member "bounding embeddings of vc classes into maximum classes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unlabeled sample compression schemes and corner peelings for ample and maximum classes")
  Member "optimal learners for multiclass problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the optimal approximation factor in density estimation")
  Member "shattering graph orientations and connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unlabeled sample compression schemes and corner peelings for ample and maximum classes")
  Member "a geometric approach to sample compression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unlabeled sample compression schemes and corner peelings for ample and maximum classes")

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)