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: "a reduction for optimizing lattice submodular functions with diminishing returns"
  Abstract: A function $f: \mathbb{Z}_+^E \rightarrow \mathbb{R}_+$ is DR-submodular if it satisfies $f(\bx + \chi_i) -f (\bx) \ge f(\by + \chi_i) - f(\by)$ for all $\bx\le \by, i\in E$. Recently, the problem of maximizing a DR-submodular function $f: \mathbb{Z}_+^E \rightarrow \mathbb{R}_+$ subject to a budget constraint $\|\bx\|_1 \leq B$ as well as additional constraints has received significant attention \cite{SKIK14,SY15,MYK15,SY16}. #R##N#In this note, we give a generic reduction from the DR-submodular setting to the submodular setting. The running time of the reduction and the size of the resulting submodular instance depends only \emph{logarithmically} on $B$. Using this reduction, one can translate the results for unconstrained and constrained submodular maximization to the DR-submodular setting for many types of constraints in a unified manner.

Connected through hyperedge HE-1 (induced by source paper "majorisation minimisation algorithms for minimising the difference between lattice submodular functions"):
  with members:
    - "continuous dr submodular maximization structure and algorithms"
    - "submodular functions from discrete to continous domains"
    - "submodular function maximization on the bounded integer lattice"
    - "maximizing monotone submodular functions over the integer lattice"
    - "algorithms for approximate minimization of the difference between submodular functions with applications"
    - "a submodular supermodular procedure with applications to discriminative structure learning"
    - "learning mixtures of submodular shells with application to document summarization"
    - "maximizing sequence submodular functions and its application to online advertising"

Connected through hyperedge HE-2 (induced by source paper "multiple knapsack constrained monotone dr submodular maximization on distributive lattice continuous greedy algorithm on median complex"):
  with members:
    - "gradient methods for submodular maximization"
    - "submodular function maximization over distributive and integer lattices"
    - "maximizing monotone submodular functions over the integer lattice"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"

Connected through hyperedge HE-3 (induced by source paper "optimal dr submodular maximization and applications to provable mean field inference"):
  with members:
    - "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "robust budget allocation via continuous submodular functions"
    - "non monotone dr submodular function maximization"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "submodular functions from discrete to continous domains"
    - "submodular function maximization on the bounded integer lattice"
    - "scalable variational inference in log supermodular models"

Connected through hyperedge HE-4 (induced by source paper "fast maximization of non submodular monotonic functions on the integer lattice"):
  with members:
    - "non monotone continuous dr submodular maximization structure and algorithms"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "non monotone dr submodular function maximization"
    - "boosting information spread an algorithmic approach"
    - "weakly submodular functions"
    - "lazier than lazy greedy"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-5 (induced by source paper "robust budget allocation via continuous submodular functions"):
  with members:
    - "robust monotone submodular function maximization"
    - "submodular optimization under noise"
    - "subquadratic submodular function minimization"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "robust influence maximization"
    - "robust influence maximization"
    - "submodular functions from discrete to continous domains"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"

Connected through hyperedge HE-6 (induced by source paper "continuous dr submodular maximization structure and algorithms"):
  with members:
    - "gradient methods for submodular maximization"
    - "robust budget allocation via continuous submodular functions"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "maximizing non monotone dr submodular functions with cardinality constraints"
    - "constrained submodular maximization beyond 1 e"
    - "submodular functions from discrete to continous domains"
    - "non negative principal component analysis message passing algorithms and sharp asymptotics"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"

Connected through hyperedge HE-7 (induced by source paper "gradient methods for submodular maximization"):
  with members:
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "subquadratic submodular function minimization"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "submodular functions from discrete to continous domains"
    - "sharp time data tradeoffs for linear inverse problems"
    - "near optimally teaching the crowd to classify"
    - "submodular optimization with submodular cover and submodular knapsack constraints"

Connected through hyperedge HE-8 (induced by source paper "equilibrium computation and robust optimization in zero sum games with submodular structure"):
  with members:
    - "robust optimization for non convex objectives"
    - "robust budget allocation via continuous submodular functions"
    - "algorithmic persuasion with no externalities"
    - "the mysteries of security games equilibrium computation becomes combinatorial algorithm design"
    - "correlation robust stochastic optimization"

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)