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: "maximizing symmetric submodular functions"
  Abstract: Symmetric submodular functions are an important family of submodular functions capturing many interesting cases including cut functions of graphs and hypergraphs. Maximization of such functions subject to various constraints receives little attention by current research, unlike similar minimization problems which have been widely studied. In this work, we identify a few submodular maximization problems for which one can get a better approximation for symmetric objectives than the state of the art approximation for general submodular functions. #R##N#We first consider the problem of maximizing a non-negative symmetric submodular function $f\colon 2^\mathcal{N} \to \mathbb{R}^+$ subject to a down-monotone solvable polytope $\mathcal{P} \subseteq [0, 1]^\mathcal{N}$. For this problem we describe an algorithm producing a fractional solution of value at least $0.432 \cdot f(OPT)$, where $OPT$ is the optimal integral solution. Our second result considers the problem $\max \{f(S) : |S| = k\}$ for a non-negative symmetric submodular function $f\colon 2^\mathcal{N} \to \mathbb{R}^+$. For this problem, we give an approximation ratio that depends on the value $k / |\mathcal{N}|$ and is always at least $0.432$. Our method can also be applied to non-negative non-symmetric submodular functions, in which case it produces $1/e - o(1)$ approximation, improving over the best known result for this problem. For unconstrained maximization of a non-negative symmetric submodular function we describe a deterministic linear-time $1/2$-approximation algorithm. Finally, we give a $[1 - (1 - 1/k)^{k - 1}]$-approximation algorithm for Submodular Welfare with $k$ players having identical non-negative submodular utility functions, and show that this is the best possible approximation ratio for the problem.

Connected through hyperedge HE-1 (induced by source paper "optimal streaming algorithms for submodular maximization with cardinality constraints"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "do less get more streaming submodular maximization with subsampling"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "constrained submodular maximization beyond 1 e"
    - "the power of randomization distributed submodular maximization on massive datasets"

Connected through hyperedge HE-2 (induced by source paper "making a sieve random improved semi streaming algorithm for submodular maximization under a cardinality constraint"):
  with members:
    - "deterministic 1 2 epsilon approximation for submodular maximization over a matroid"
    - "online submodular maximization beating 1 2 made simple"
    - "do less get more streaming submodular maximization with subsampling"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "constrained submodular maximization beyond 1 e"
    - "streaming algorithms for submodular function maximization"
    - "online submodular maximization with preemption"

Connected through hyperedge HE-3 (induced by source paper "guess free maximization of submodular and linear sums"):
  with members:
    - "random order contention resolution schemes"
    - "a new approximation guarantee for monotone submodular function maximization via discrete convexity"
    - "constrained submodular maximization beyond 1 e"
    - "online contention resolution schemes"
    - "a stochastic probing problem with applications"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"

Connected through hyperedge HE-4 (induced by source paper "weakly submodular maximization beyond cardinality constraints does randomization help greedy"):
  with members:
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "submodular variational inference for network reconstruction"
    - "why should i trust you explaining the predictions of any classifier"
    - "constrained submodular maximization via a non symmetric technique"
    - "constrained submodular maximization beyond 1 e"
    - "building a good team secretary problems and the supermodular degree"
    - "constrained monotone function maximization and the supermodular degree"
    - "lazier than lazy greedy"

Connected through hyperedge HE-5 (induced by source paper "a deterministic algorithm for maximizing submodular functions"):
  with members:
    - "on the hardness of welfare maximization in combinatorial auctions with submodular valuations"
    - "combinatorial auctions with decreasing marginal utilities"

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)