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 "neural network support vector detection via a soft label hybrid k means classifier"):
  Abstract: We use random geometric graphs to describe clusters of higher dimensional data points which are bijectively mapped to a (possibly) lower dimensional space where an equivalent random cluster model is used to calculate the expected number of modes to be found when separating the data of a multi-modal data set into distinct clusters. Furthermore, as a function of the expected number of modes and the number of data points in the sample, an upper bound on a given distance measure is found such that data points have the greatest correlation if their mutual distances from a common center is less than or equal to the calculated bound. Anomalies are exposed, which lie outside of the union of all regularized clusters of data points. #R##N#Similar to finding a hyperplane which can be shifted along its normal to expose the maximal distance between binary classes, it is shown that the union of regularized clusters can be used to define a hyperplane which can be shifted by a certain amount to separate the data into binary classes and that the shifted hyperplane defines the activation function for a two-class discriminating neural network. Lastly, this neural network is used to detect the set of support vectors which determines the maximally-separating region between the binary classes.

Includes members:
  Member "continuum limits of markov chains with application to network modeling":
    also connected through other hyperedges:
      (none)
  Member "lower bounds for embedding into distributions over excluded minor graph families":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a 1 varepsilon embedding of low highway dimension graphs into bounded treewidth graphs")

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)