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 function maximization on the bounded integer lattice"
  Abstract: We consider the problem of maximizing a submodular function on the bounded integer lattice. As a direct generalization of submodular set functions, $f: \{0, \ldots, C\} \rightarrow \mathbb{R}_+$ is submodular, if $f(x) + f(y) \geq f(x \land y) + f(x \lor y)$ for all $x,y \in \{0, \ldots, C\}$ where $\land$ and $\lor$ denote element-wise minimum and maximum. The objective is finding a vector $x$ maximizing $f(x)$. In this paper, we present a deterministic $\frac{1}{3}$-approximation using a framework inspired by Buchbinder et al. We also provide an example that shows the analysis is tight and yields additional insight into the possibilities of modifying the algorithm. Moreover, we examine some structural differences to maximization of submodular set functions which make our problem harder to solve.

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"
    - "a reduction for optimizing lattice submodular functions with diminishing returns"
    - "submodular functions from discrete to continous domains"
    - "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 "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"
    - "a reduction for optimizing lattice submodular functions with diminishing returns"
    - "submodular functions from discrete to continous domains"
    - "scalable variational inference in log supermodular models"

Connected through hyperedge HE-3 (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"
    - "a reduction for optimizing lattice submodular functions with diminishing returns"
    - "submodular functions from discrete to continous domains"

Connected through hyperedge HE-4 (induced by source paper "guaranteed non convex optimization submodular maximization over continuous domains"):
  with members:
    - "deep submodular functions"
    - "a reduction for optimizing lattice submodular functions with diminishing returns"
    - "submodular functions from discrete to continous domains"
    - "on graduated optimization for stochastic non convex problems"
    - "maximizing monotone submodular functions over the integer lattice"
    - "tensor decompositions for learning latent variable models"
    - "near optimal nonmyopic value of information in graphical models"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "non monotone dr submodular function maximization"):
  with members:
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "a reduction for optimizing lattice submodular functions with diminishing returns"
    - "maximizing submodular functions with the diminishing return property over the integer lattice"
    - "deterministic algorithms for submodular maximization problems"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "submodular function maximization over distributive and integer lattices"):
  with members:
    - "non monotone dr submodular function maximization"
    - "deterministic algorithms for submodular maximization problems"
    - "symmetry and approximability of submodular maximization problems"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "submodular maximization by simulated annealing"
    - "submodular approximation sampling based algorithms and lower bounds"

Connected through hyperedge HE-7 (induced by source paper "maximizing monotone submodular functions over the integer lattice"):
  with members:
    - "improved approximation algorithms for k submodular function maximization"
    - "deterministic algorithms for submodular maximization problems"
    - "how to influence people with partial incentives"
    - "online submodular welfare maximization greedy is optimal"

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.DM(Discrete Mathematics)