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: "gradient methods for submodular maximization"
  Abstract: In this paper, we study the problem of maximizing continuous submodular functions that naturally arise in many learning applications such as those involving utility functions in active learning and sensing, matrix approximations and network inference. Despite the apparent lack of convexity in such functions, we prove that stochastic projected gradient methods can provide strong approximation guarantees for maximizing continuous submodular functions with convex constraints. More specifically, we prove that for monotone continuous DR-submodular functions, all fixed points of projected gradient ascent provide a factor $1/2$ approximation to the global maxima. We also study stochastic gradient and mirror methods and show that after $\mathcal{O}(1/\epsilon^2)$ iterations these methods reach solutions which achieve in expectation objective values exceeding $(\frac{\text{OPT}}{2}-\epsilon)$. An immediate application of our results is to maximize submodular functions that are defined stochastically, i.e. the submodular function is defined as an expectation over a family of submodular functions with an unknown distribution. We will show how stochastic gradient methods are naturally well-suited for this setting, leading to a factor $1/2$ approximation when the function is monotone. In particular, it allows us to approximately maximize discrete, monotone submodular optimization problems via projected gradient descent on a continuous relaxation, directly connecting the discrete and continuous domains. Finally, experiments on real data demonstrate that our projected gradient methods consistently achieve the best utility compared to other continuous baselines while remaining competitive in terms of computational effort.

Connected through hyperedge HE-1 (induced by source paper "black box submodular maximization discrete and continuous settings"):
  with members:
    - "introduction to online convex optimization"
    - "online continuous submodular maximization from full information to bandit feedback"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "black box adversarial attacks with limited queries and information"
    - "discrete adversarial attacks and submodular optimization with applications to text classification"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "continuous dr submodular maximization structure and algorithms"
    - "robust budget allocation via continuous submodular functions"

Connected through hyperedge HE-2 (induced by source paper "adaptive sequence submodularity"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "data summarization at scale a two stage submodular approach"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "do less get more streaming submodular maximization with subsampling"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"

Connected through hyperedge HE-3 (induced by source paper "online non monotone dr submodular maximization"):
  with members:
    - "introduction to online convex optimization"
    - "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
    - "an optimal algorithm for online unconstrained submodular maximization"
    - "non monotone continuous dr submodular maximization structure and algorithms"
    - "robust budget allocation via continuous submodular functions"
    - "potential function proofs for first order methods"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "the power of randomization distributed submodular maximization on massive datasets"

Connected through hyperedge HE-4 (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"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "submodular optimization under noise"
    - "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-5 (induced by source paper "non monotone dr submodular maximization approximation and regret guarantees"):
  with members:
    - "introduction to online convex optimization"
    - "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
    - "an optimal algorithm for online unconstrained submodular maximization"
    - "non monotone continuous dr submodular maximization structure and algorithms"
    - "robust budget allocation via continuous submodular functions"
    - "non monotone dr submodular function maximization"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "submodular functions from discrete to continous domains"

Connected through hyperedge HE-6 (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"
    - "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-7 (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"
    - "stochastic submodular maximization the case of coverage functions"
    - "democratic fair allocation of indivisible goods"

Connected through hyperedge HE-8 (induced by source paper "scalable lattice influence maximization"):
  with members:
    - "an issue in the martingale analysis of the influence maximization algorithm imm"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "how to influence people with partial incentives"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "maximizing social influence in nearly optimal time"
    - "irie scalable and robust influence maximization in social networks"

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)