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 "graph heat mixture model learning"):
  Abstract: Graph inference methods have recently attracted a great interest from the scientific community, due to the large value they bring in data interpretation and analysis. However, most of the available state-of-the-art methods focus on scenarios where all available data can be explained through the same graph, or groups corresponding to each graph are known a priori. In this paper, we argue that this is not always realistic and we introduce a generative model for mixed signals following a heat diffusion process on multiple graphs. We propose an expectation-maximisation algorithm that can successfully separate signals into corresponding groups, and infer multiple graphs that govern their behaviour. We demonstrate the benefits of our method on both synthetic and real data.

Includes members:
  Member "learning graphs from data a signal representation perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")
  Member "graph laplacian mixture model":
    also connected through other hyperedges:
      (none)
  Member "graph learning from filtered signals graph system and diffusion kernel identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning graphs from data a signal representation perspective")
  Member "graph learning under sparsity priors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning graphs from data a signal representation perspective")
  Member "network topology inference from spectral templates":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")
  Member "learning heat diffusion graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")
  Member "graph learning from data under laplacian and structural constraints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exact blind community detection from signals on multiple graphs")
  Member "learning laplacian matrix in smooth graph signal representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")

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)