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: "fast semidifferential based submodular function optimization"
  Abstract: We present a practical and powerful new framework for both unconstrained and constrained submodular function optimization based on discrete semidifferentials (sub- and super-differentials). The resulting algorithms, which repeatedly compute and then efficiently optimize submodular semigradients, offer new and generalize many old methods for submodular optimization. Our approach, moreover, takes steps towards providing a unifying paradigm applicable to both submodular min- imization and maximization, problems that historically have been treated quite distinctly. The practicality of our algorithms is important since interest in submodularity, owing to its natural and wide applicability, has recently been in ascendance within machine learning. We analyze theoretical properties of our algorithms for minimization and maximization, and show that many state-of-the-art maximization algorithms are special cases. Lastly, we complement our theoretical analyses with supporting empirical experiments.

Connected through hyperedge HE-1 (induced by source paper "robust submodular minimization with applications to cooperative modeling"):
  with members:
    - "near optimal algorithms for hard submodular programs with discounted cooperative costs"
    - "approximating some network problems with scenarios"
    - "robust optimization for non convex objectives"
    - "robust submodular maximization offline and online algorithms"
    - "equilibrium computation and robust optimization in zero sum games with submodular structure"
    - "polyhedral aspects of submodularity convexity and concavity"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"

Connected through hyperedge HE-2 (induced by source paper "a unified framework of robust submodular optimization"):
  with members:
    - "near optimal algorithms for hard submodular programs with discounted cooperative costs"
    - "learning from less data a unified data subset selection and active learning framework for computer vision"
    - "robust monotone submodular function maximization"
    - "distributionally robust submodular maximization"
    - "approximating some network problems with scenarios"
    - "a framework towards domain specific video summarization"
    - "robust optimization for non convex objectives"
    - "robust submodular maximization offline and online algorithms"

Connected through hyperedge HE-3 (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"
    - "submodular optimization under noise"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "resilient active target tracking with multiple robots"):
  with members:
    - "resilient active information gathering with mobile robots"

Connected through hyperedge HE-5 (induced by source paper "submodular trajectory optimization for aerial 3d scanning"):
  with members:
    - "unrealcv connecting computer vision to unreal engine"
    - "autonomous exploration with a low cost quadrocopter using semi dense monocular slam"
    - "virtual rephotography novel view prediction error for 3d reconstruction"
    - "learning to gather information via imitation"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "efficient informative sensing using multiple robots"

Connected through hyperedge HE-6 (induced by source paper "finding optimal sinks for random walkers in a network"):
  with members:
    - "tight bounds for rumor spreading with vertex expansion"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"

Connected through hyperedge HE-7 (induced by source paper "submodular variational inference for network reconstruction"):
  with members:
    - "polyhedral aspects of submodularity convexity and concavity"
    - "seeing the unseen network inferring hidden social ties from respondent driven sampling"
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "submodular inference of diffusion networks from multiple trees"
    - "uncovering the temporal dynamics of diffusion networks"
    - "topology discovery of sparse random graphs with few participants"

Connected through hyperedge HE-8 (induced by source paper "on the reducibility of submodular functions"):
  with members:
    - "lazier than lazy greedy"
    - "on unconstrained quasi submodular function optimization"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "reflection methods for user friendly submodular optimization"
    - "learning with submodular functions a convex optimization perspective"
    - "adaptive submodularity theory and applications in active learning and 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)