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: "submodular functions extensions distributions and algorithms a survey"
  Abstract: Submodularity is a fundamental phenomenon in combinatorial optimization. Submodular functions occur in a variety of combinatorial settings such as coverage problems, cut problems, welfare maximization, and many more. Therefore, a lot of work has been concerned with maximizing or minimizing a submodular function, often subject to combinatorial constraints. Many of these algorithmic results exhibit a common structure. Namely, the function is extended to a continuous, usually non-linear, function on a convex domain. Then, this relaxation is solved, and the fractional solution rounded to yield an integral solution. Often, the continuous extension has a natural interpretation in terms of distributions on subsets of the ground set. This interpretation is often crucial to the results and their analysis. The purpose of this survey is to highlight this connection between extensions, distributions, relaxations, and optimization in the context of submodular functions. We also present the first constant factor approximation algorithm for minimizing symmetric submodular functions subject to a cardinality constraint.

Connected through hyperedge HE-1 (induced by source paper "optimal approximation for unconstrained non submodular minimization"):
  with members:
    - "near optimal approximate discrete and continuous submodular function minimization"
    - "fast maximization of non submodular monotonic functions on the integer lattice"
    - "on maximization of weakly modular functions guarantees of multi stage algorithms tractability and hardness"
    - "gradient methods for submodular maximization"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "submodular optimization under noise"
    - "subquadratic submodular function minimization"
    - "tolerant junta testing and the connection to submodular optimization and function isomorphism"

Connected through hyperedge HE-2 (induced by source paper "on the tractability of public persuasion with no externalities"):
  with members:
    - "algorithmic persuasion with no externalities"
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "no regret learning in bayesian games"
    - "algorithmic bayesian persuasion"
    - "deterministic algorithms for submodular maximization problems"
    - "hardness results for signaling in bayesian zero sum and network routing games"
    - "honest signaling in zero sum games is hard and lying is even harder"
    - "mixture selection mechanism design and signaling"

Connected through hyperedge HE-3 (induced by source paper "an optimal algorithm for online unconstrained submodular maximization"):
  with members:
    - "deterministic algorithms for submodular maximization problems"

Connected through hyperedge HE-4 (induced by source paper "combinatorial penalties which structures are preserved by convex relaxations"):
  with members:
    - "gradient methods for submodular maximization"
    - "a totally unimodular view of structured sparsity"
    - "near optimal nonmyopic value of information in graphical models"
    - "optimization with sparsity inducing penalties"
    - "learning with submodular functions a convex optimization perspective"
    - "structured sparsity inducing norms through submodular functions"
    - "model based compressive sensing"

Connected through hyperedge HE-5 (induced by source paper "randomized bicriteria approximation algorithm for minimum submodular cost partial multi cover problem"):
  with members:
    - "analytical approach to parallel repetition"
    - "submodular cost allocation problem and applications"

Connected through hyperedge HE-6 (induced by source paper "computational aspects of private bayesian persuasion"):
  with members:
    - "algorithmic bayesian persuasion"

Connected through hyperedge HE-7 (induced by source paper "structured sparsity discrete and convex approaches"):
  with members:
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "group sparse model selection hardness and relaxations"
    - "a novel sparsity and clustering regularization"
    - "on model based rip 1 matrices"
    - "sparse inverse covariance matrix estimation using quadratic approximation"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-8 (induced by source paper "maximizing symmetric submodular functions"):
  with members:
    - "constrained submodular maximization via a non symmetric technique"
    - "constrained submodular maximization beyond 1 e"
    - "improved approximation algorithms for k submodular function maximization"
    - "deterministic algorithms for submodular maximization problems"
    - "local distribution and the symmetry gap approximability of multiway partitioning problems"
    - "on the approximation of submodular functions"
    - "symmetry and approximability of submodular maximization problems"
    - "approximation algorithms for submodular multiway partition"

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.DS(Data Structures and Algorithms)