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: "a geometric approach to sample compression"
  Abstract: The Sample Compression Conjecture of Littlestone & Warmuth has remained unsolved for over two decades. This paper presents a systematic geometric investigation of the compression of finite maximum concept classes. Simple arrangements of hyperplanes in Hyperbolic space, and Piecewise-Linear hyperplane arrangements, are shown to represent maximum classes, generalizing the corresponding Euclidean result. A main result is that PL arrangements can be swept by a moving hyperplane to unlabeled d-compress any finite maximum class, forming a peeling scheme as conjectured by Kuzmin & Warmuth. A corollary is that some d-maximal classes cannot be embedded into any maximum class of VC dimension d+k, for any constant k. The construction of the PL sweeping involves Pachner moves on the one-inclusion graph, corresponding to moves of a hyperplane across the intersection of d other hyperplanes. This extends the well known Pachner moves for triangulations to cubical complexes.

Connected through hyperedge HE-1 (induced by source paper "unlabeled sample compression schemes and corner peelings for ample and maximum classes"):
  with members:
    - "labeled compression schemes for extremal classes"
    - "sample compression schemes for vc classes"
    - "bounding embeddings of vc classes into maximum classes"
    - "shattering graph orientations and connectivity"

Connected through hyperedge HE-2 (induced by source paper "the complexity of learning acyclic conditional preference networks"):
  with members:
    - "labeled compression schemes for extremal classes"
    - "the computational complexity of dominance and consistency in cp nets"
    - "cp nets a tool for representing and reasoning with conditional ceteris paribus preference statements"

Connected through hyperedge HE-3 (induced by source paper "on statistical learning via the lens of compression"):
  with members:
    - "adaptive learning with robust generalization guarantees"
    - "nearly optimal classification for semimetrics"
    - "sample compression schemes for vc classes"
    - "optimal learners for multiclass problems"
    - "multiclass learnability and the erm principle"

Connected through hyperedge HE-4 (induced by source paper "labeled compression schemes for extremal classes"):
  with members:
    - "teaching and compressing for low vc dimension"
    - "sample compression schemes for vc classes"
    - "bounding embeddings of vc classes into maximum classes"
    - "optimal learners for multiclass problems"
    - "shattering graph orientations and connectivity"

Connected through hyperedge HE-5 (induced by source paper "sample compression schemes for vc classes"):
  with members:
    - "the optimal sample complexity of pac learning"
    - "teaching and compressing for low vc dimension"
    - "optimal learners for multiclass problems"
    - "simple strategies for large zero sum games with applications to complexity theory"

Connected through hyperedge HE-6 (induced by source paper "teaching and compressing for low vc dimension"):
  with members:
    - "sample compression schemes for vc classes"

Connected through hyperedge HE-7 (induced by source paper "bounding embeddings of vc classes into maximum classes"):
  with members:
    - "on the list decodability of random linear codes"

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)