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: "maximum margin multiclass nearest neighbors"
  Abstract: We develop a general framework for margin-based multicategory classification in metric spaces. The basic work-horse is a margin-regularized version of the nearest-neighbor classifier. We prove generalization bounds that match the state of the art in sample size $n$ and significantly improve the dependence on the number of classes $k$. Our point of departure is a nearly Bayes-optimal finite-sample risk bound independent of $k$. Although $k$-free, this bound is unregularized and non-adaptive, which motivates our main result: Rademacher and scale-sensitive margin bounds with a logarithmic dependence on $k$. As the best previous risk estimates in this setting were of order $\sqrt k$, our bound is exponentially sharper. From the algorithmic standpoint, in doubling metric spaces our classifier may be trained on $n$ examples in $O(n^2\log n)$ time and evaluated on new points in $O(\log n)$ time.

Connected through hyperedge HE-1 (induced by source paper "universal bayes consistency in metric spaces"):
  with members:
    - "a new lower bound for agnostic learning with sample compression schemes"
    - "nearest neighbor sample compression efficiency consistency infinite dimensions"
    - "rates of convergence for nearest neighbor classification"
    - "a bayes consistent 1 nn classifier"
    - "on the geometry of similarity search dimensionality curse and concentration of measure"

Connected through hyperedge HE-2 (induced by source paper "classification in asymmetric spaces via sample compression"):
  with members:
    - "a constant factor approximation algorithm for the asymmetric traveling salesman problem"
    - "effective resistance reducing flows spectrally thin trees and asymmetric tsp"
    - "efficient classification for metric data"
    - "quasi metrics similarities and searches aspects of geometry of protein datasets"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-3 (induced by source paper "maximal margin distribution support vector regression with coupled constraints based convex optimization"):
  with members:
    - "large margin distribution machine"
    - "on the doubt about margin explanation of boosting"

Connected through hyperedge HE-4 (induced by source paper "data dependent generalization bounds for multi class classification"):
  with members:
    - "generalization error bounds for learning to rank does the length of document lists matter"
    - "a vector contraction inequality for rademacher complexities"
    - "lshtc a benchmark for large scale text classification"
    - "multi class svms from tighter data dependent generalization bounds to novel algorithms"
    - "efficient large scale video classification"
    - "conditional probability tree estimation analysis and algorithms"
    - "multiclass learnability and the erm principle"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-5 (induced by source paper "nearest neighbor sample compression efficiency consistency infinite dimensions"):
  with members:
    - "active nearest neighbor learning in metric spaces"
    - "rates of convergence for nearest neighbor classification"
    - "a bayes consistent 1 nn classifier"
    - "adaptive metric dimensionality reduction"
    - "on the geometry of similarity search dimensionality curse and concentration of measure"

Connected through hyperedge HE-6 (induced by source paper "active nearest neighbor learning in metric spaces"):
  with members:
    - "exact lower bounds for the agnostic probably approximately correct pac machine learning model"
    - "minimax analysis of active learning"
    - "active learning on trees and graphs"
    - "adaptive metric dimensionality reduction"
    - "efficient active learning of halfspaces an aggressive approach"

Connected through hyperedge HE-7 (induced by source paper "nearly optimal classification for semimetrics"):
  with members:
    - "near optimal sample compression for nearest neighbors"
    - "fast construction of nets in low dimensional metrics and their applications"
    - "planar earthmover is not in l_1"

Connected through hyperedge HE-8 (induced by source paper "near optimal sample compression for nearest neighbors"):
  with members:
    - "nearest neighbor sample compression efficiency consistency infinite dimensions"
    - "active nearest neighbor learning in metric spaces"
    - "rates of convergence for nearest neighbor classification"
    - "a bayes consistent 1 nn classifier"
    - "efficient classification for metric data"
    - "adaptive metric dimensionality reduction"
    - "efficient regression in metric spaces via approximate lipschitz extension"
    - "fast construction of nets in low dimensional metrics and their applications"

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)