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 optimization under noise"
  Abstract: We consider the problem of maximizing a monotone submodular function under noise. There has been a great deal of work on optimization of submodular functions under various constraints, resulting in algorithms that provide desirable approximation guarantees. In many applications, however, we do not have access to the submodular function we aim to optimize, but rather to some erroneous or noisy version of it. This raises the question of whether provable guarantees are obtainable in presence of error and noise. We provide initial answers, by focusing on the question of maximizing a monotone submodular function under a cardinality constraint when given access to a noisy oracle of the function. We show that: #R##N#- For a cardinality constraint $k \geq 2$, there is an approximation algorithm whose approximation ratio is arbitrarily close to $1-1/e$; #R##N#- For $k=1$ there is an algorithm whose approximation ratio is arbitrarily close to $1/2$. No randomized algorithm can obtain an approximation ratio better than $1/2+o(1)$; #R##N#-If the noise is adversarial, no non-trivial approximation guarantee can be obtained.

Connected through hyperedge HE-1 (induced by source paper "searching trees with permanently noisy advice walking and query algorithms"):
  with members:
    - "binary search in graphs revisited"
    - "a general framework for robust interactive learning"
    - "deterministic and probabilistic binary search in graphs"
    - "noisy sorting without resampling"

Connected through hyperedge HE-2 (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"
    - "subquadratic submodular function minimization"
    - "tolerant junta testing and the connection to submodular optimization and function isomorphism"
    - "structured sparsity discrete and convex approaches"

Connected through hyperedge HE-3 (induced by source paper "submodular cost submodular cover with an approximate oracle"):
  with members:
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "cost effective seed selection in online social networks"
    - "why approximate when you can get the exact optimal targeted viral marketing at scale"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "scalable bicriteria algorithms for the threshold activation problem in online social networks"
    - "an efficient streaming algorithm for the submodular cover problem"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "influence maximization near optimal time complexity meets practical efficiency"

Connected through hyperedge HE-4 (induced by source paper "a memoization framework for scaling submodular optimization to large scale problems"):
  with members:
    - "stochastic submodular maximization the case of coverage functions"
    - "gradient methods for submodular maximization"
    - "deep submodular functions"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "provable submodular minimization using wolfe s algorithm"
    - "lazier than lazy greedy"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"

Connected through hyperedge HE-5 (induced by source paper "scheduling with predictions and the price of misprediction"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "beyond worst case analysis"
    - "competitive caching with machine learned advice"
    - "the case for learned index structures"
    - "soap one clean analysis of all age based scheduling policies"
    - "the limitations of optimization from samples"
    - "satzilla portfolio based algorithm selection for sat"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-6 (induced by source paper "maximizing monotone dr submodular continuous functions by derivative free optimization"):
  with members:
    - "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
    - "gradient methods for submodular maximization"
    - "continuous dr submodular maximization structure and algorithms"
    - "robust budget allocation via continuous submodular functions"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "submodular functions from discrete to continous domains"
    - "maximizing monotone submodular functions over the integer lattice"
    - "online submodular welfare maximization greedy is optimal"

Connected through hyperedge HE-7 (induced by source paper "robust budget allocation via continuous submodular functions"):
  with members:
    - "robust monotone submodular function maximization"
    - "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"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"

Connected through hyperedge HE-8 (induced by source paper "deep submodular functions"):
  with members:
    - "a new framework for distributed submodular maximization"
    - "towards minimizing k submodular functions"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"
    - "efficient minimization of decomposable submodular functions"
    - "submodular functions learnability structure and 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)