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: "stochastic submodular maximization the case of coverage functions"
  Abstract: Stochastic optimization of continuous objectives is at the heart of modern machine learning. However, many important problems are of discrete nature and often involve submodular objectives. We seek to unleash the power of stochastic continuous optimization, namely stochastic gradient descent and its variants, to such discrete problems. We first introduce the problem of stochastic submodular optimization, where one needs to optimize a submodular objective which is given as an expectation. Our model captures situations where the discrete objective arises as an empirical risk (e.g., in the case of exemplar-based clustering), or is given as an explicit stochastic model (e.g., in the case of influence maximization in social networks). By exploiting that common extensions act linearly on the class of submodular functions, we employ projected stochastic gradient ascent and its variants in the continuous domain, and perform rounding to obtain discrete solutions. We focus on the rich and widely used family of weighted coverage functions. We show that our approach yields solutions that are guaranteed to match the optimal approximation guarantees, while reducing the computational cost by several orders of magnitude, as we demonstrate empirically.

Connected through hyperedge HE-1 (induced by source paper "end to end learning and optimization on graphs"):
  with members:
    - "gap generalizable approximate graph partitioning framework"
    - "generalization bounds in the predict then optimize framework"
    - "link prediction based on graph neural networks"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "hierarchical graph representation learning with differentiable pooling"
    - "optnet differentiable optimization as a layer in neural networks"
    - "neural expectation maximization"
    - "task based end to end model learning in stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "seeding with costly network information"):
  with members:
    - "beyond worst case in approximability of nonsubmodular influence maximization"
    - "metric sublinear algorithms via linear sampling"
    - "the importance of communities for learning to influence"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "robust influence maximization"
    - "almost optimal streaming algorithms for coverage problems"
    - "online influence maximization"
    - "scalable methods for adaptively seeding a social network"

Connected through hyperedge HE-3 (induced by source paper "a memoization framework for scaling submodular optimization to large scale problems"):
  with members:
    - "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"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"

Connected through hyperedge HE-4 (induced by source paper "group fairness in influence maximization"):
  with members:
    - "fair allocation of indivisible public goods"
    - "groupwise maximin fair allocation of indivisible goods"
    - "multi objective maximization of monotone submodular functions with cardinality constraint"
    - "diverse weighted bipartite b matching"
    - "approximate maximin shares for groups of agents"
    - "multiwinner elections with diversity constraints"
    - "gradient methods for submodular maximization"
    - "democratic fair allocation of indivisible goods"

Connected through hyperedge HE-5 (induced by source paper "distributionally robust submodular maximization"):
  with members:
    - "robust monotone submodular function maximization"
    - "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"
    - "gradient methods for submodular maximization"
    - "robust budget allocation via continuous submodular functions"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-6 (induced by source paper "defending elections against malicious spread of misinformation"):
  with members:
    - "introduction to online convex optimization"
    - "equilibrium computation and robust optimization in zero sum games with submodular structure"
    - "gradient methods for submodular maximization"
    - "robust budget allocation via continuous submodular functions"
    - "elections with few voters candidate control can be easy"
    - "multimode control attacks on elections"

Connected through hyperedge HE-7 (induced by source paper "scalable influence maximization with general marketing strategies"):
  with members:
    - "gradient methods for submodular maximization"
    - "guaranteed non convex optimization submodular maximization over continuous domains"

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.LG(Machine Learning)