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 "clustering aware graph construction a joint learning perspective"):
  Abstract: Graph-based clustering methods have demonstrated the effectiveness in various applications. Generally, existing graph-based clustering methods first construct a graph to represent the input data and then partition it to generate the clustering result. However, such a stepwise manner may make the constructed graph not fit the requirements for the subsequent decomposition, leading to compromised clustering accuracy. To this end, we propose a joint learning framework, which is able to learn the graph and the clustering result simultaneously, such that the resulting graph is tailored to the clustering task. The proposed model is formulated as a well-defined nonnegative and off-diagonal constrained optimization problem, which is further efficiently solved with convergence theoretically guaranteed. The advantage of the proposed model is demonstrated by comparing with 19 state-of-the-art clustering methods on 10 datasets with 4 clustering metrics.

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 "joint label prediction based semi supervised adaptive concept factorization for robust data representation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning hybrid representation by robust dictionary learning in factorized compressed space")
  Member "structured sparse subspace clustering a joint affinity learning and subspace clustering framework":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self expressive subspace clustering to recognize motion dynamics of a multi joint coordination for chronic ankle instability")
  Member "constructing the l2 graph for robust subspace learning and subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "graph learning from data under laplacian and structural constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exact blind community detection from signals on multiple graphs")
  Member "learning sparse graphs under smoothness prior":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")
  Member "robust principal component analysis on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "wasserstein based graph alignment")
  Member "learning laplacian matrix in smooth graph signal representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)