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 "convex programming based spectral clustering"):
  Abstract: Clustering is a fundamental task in data analysis, and spectral clustering has been recognized as a promising approach to it. Given a graph describing the relationship between data, spectral clustering explores the underlying cluster structure in two stages. The first stage embeds the nodes of the graph into real space, and the second stage groups the embedded nodes into several clusters. The use of the $k$-means method in the grouping stage is currently standard practice. We present a spectral clustering algorithm that uses convex programming in the grouping stage, and study how well it works. The concept behind the algorithm design lies in the following observation. The nodes with the largest degree in each cluster may be found by computing an enclosing ellipsoid for embedded nodes in real space, and the clusters may be identified by using those nodes. We show that the observations are valid, and the algorithm returns clusters to provide the conductance of graph, if the gap assumption, introduced by Peng el al. at COLT 2015, is satisfied. We also give an experimental assessment of the algorithm's performance.

Includes members:
  Member "emnist an extension of mnist to handwritten letters":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "fashion mnist a novel image dataset for benchmarking machine learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "spectral clustering by ellipsoid and its connection to separable nonnegative matrix factorization":
    also connected through other hyperedges:
      (none)
  Member "a note on spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "partitioning well clustered graphs spectral clustering works":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "partitioning into expanders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient algorithms for the potts model on small set expanders")
  Member "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient algorithms for the potts model on small set expanders")
  Member "defining and evaluating network communities based on ground truth":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")

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)