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: "structured convex optimization under submodular constraints"
  Abstract: A number of discrete and continuous optimization problems in machine learning are related to convex minimization problems under submodular constraints. In this paper, we deal with a submodular function with a directed graph structure, and we show that a wide range of convex optimization problems under submodular constraints can be solved much more efficiently than general submodular optimization methods by a reduction to a maximum flow problem. Furthermore, we give some applications, including sparse optimization methods, in which the proposed methods are effective. Additionally, we evaluate the performance of the proposed method through computational experiments.

Connected through hyperedge HE-1 (induced by source paper "attaining fairness in communication for omniscience"):
  with members:
    - "a faster algorithm for asymptotic communication for omniscience"
    - "fairest constant sum rate transmission for cooperative data exchange an m convex minimization approach"
    - "coded cooperative data exchange in multihop networks"
    - "on coding for cooperative data exchange"
    - "on tightness of mutual dependence upperbound for secret key capacity of multiple terminals"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

Connected through hyperedge HE-2 (induced by source paper "communication for omniscience"):
  with members:
    - "fairness in communication for omniscience"
    - "a faster algorithm for asymptotic communication for omniscience"
    - "info clustering a mathematical theory for data clustering"
    - "estimating minimum sum rate for cooperative data exchange"
    - "iterative merging algorithm for cooperative data exchange"
    - "fairest constant sum rate transmission for cooperative data exchange an m convex minimization approach"
    - "coded cooperative data exchange in multihop networks"
    - "perfect omniscience perfect secrecy and steiner tree packing"

Connected through hyperedge HE-3 (induced by source paper "fairness in communication for omniscience"):
  with members:
    - "a game theoretic perspective on communication for omniscience"
    - "estimating minimum sum rate for cooperative data exchange"
    - "fairest constant sum rate transmission for cooperative data exchange an m convex minimization approach"
    - "efficient algorithms for the data exchange problem"
    - "coded cooperative data exchange in multihop networks"
    - "on coding for cooperative data exchange"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

Connected through hyperedge HE-4 (induced by source paper "parametric maxflows for structured sparse learning with convex relaxations of submodular functions"):
  with members:
    - "sparse modeling for image and vision processing"
    - "structured sparsity through convex optimization"
    - "learning with submodular functions a convex optimization perspective"
    - "shaping level sets with submodular functions"
    - "structured sparsity inducing norms through submodular functions"

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)