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: "sample compression schemes for vc classes"
  Abstract: Sample compression schemes were defined by Littlestone and Warmuth (1986) as an abstraction of the structure underlying many learning algorithms. Roughly speaking, a sample compression scheme of size $k$ means that given an arbitrary list of labeled examples, one can retain only $k$ of them in a way that allows to recover the labels of all other examples in the list. They showed that compression implies PAC learnability for binary-labeled classes, and asked whether the other direction holds. We answer their question and show that every concept class $C$ with VC dimension $d$ has a sample compression scheme of size exponential in $d$. The proof uses an approximate minimax phenomenon for binary matrices of low VC dimension, which may be of interest in the context of game theory.

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"
    - "teaching and compressing for low vc dimension"

Connected through hyperedge HE-2 (induced by source paper "learning from weakly dependent data under dobrushin s condition"):
  with members:
    - "on statistical learning via the lens of compression"
    - "testing ising models"
    - "social contagion theory examining dynamic social networks and human behavior"
    - "predictive pac learnability a paradigm for learning from exchangeable input data"

Connected through hyperedge HE-3 (induced by source paper "bounds in query learning"):
  with members:
    - "regular omega languages with an informative right congruence"

Connected through hyperedge HE-4 (induced by source paper "average case information complexity of learning"):
  with members:
    - "a direct sum result for the information complexity of learning"
    - "chaining mutual information and tightening generalization bounds"
    - "calibrating noise to variance in adaptive data analysis"
    - "learners that use little information"
    - "algorithmic stability for adaptive data analysis"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-5 (induced by source paper "a direct sum result for the information complexity of learning"):
  with members:
    - "learners that use little information"
    - "max information differential privacy and post selection hypothesis testing"
    - "algorithmic stability for adaptive data analysis"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-6 (induced by source paper "unlabeled sample compression schemes and corner peelings for ample and maximum classes"):
  with members:
    - "labeled compression schemes for extremal classes"
    - "bounding embeddings of vc classes into maximum classes"
    - "shattering graph orientations and connectivity"
    - "a geometric approach to sample compression"

Connected through hyperedge HE-7 (induced by source paper "on the perceptron s compression"):
  with members:
    - "perceptron mistake bounds"

Connected through hyperedge HE-8 (induced by source paper "a new lower bound for agnostic learning with sample compression schemes"):
  with members:
    - "the optimal sample complexity of pac learning"

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)