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: "subquadratic submodular function minimization"
  Abstract: Submodular function minimization (SFM) is a fundamental discrete optimization problem which generalizes many well known problems, has applications in various fields, and can be solved in polynomial time. Owing to applications in computer vision and machine learning, fast SFM algorithms are highly desirable. The current fastest algorithms [Lee, Sidford, Wong, FOCS 2015] run in $O(n^{2}\log nM\cdot\textrm{EO} +n^{3}\log^{O(1)}nM)$ time and $O(n^{3}\log^{2}n\cdot \textrm{EO} +n^{4}\log^{O(1)}n$) time respectively, where $M$ is the largest absolute value of the function (assuming the range is integers) and $\textrm{EO}$ is the time taken to evaluate the function on any set. Although the best known lower bound on the query complexity is only $\Omega(n)$, the current shortest non-deterministic proof certifying the optimum value of a function requires $\Theta(n^{2})$ function evaluations. #R##N#The main contribution of this paper are subquadratic SFM algorithms. For integer-valued submodular functions, we give an SFM algorithm which runs in $O(nM^{3}\log n\cdot\textrm{EO})$ time giving the first nearly linear time algorithm in any known regime. For real-valued submodular functions with range in $[-1,1]$, we give an algorithm which in $\tilde{O}(n^{5/3}\cdot\textrm{EO}/\varepsilon^{2})$ time returns an $\varepsilon$-additive approximate solution. At the heart of it, our algorithms are projected stochastic subgradient descent methods on the Lovasz extension of submodular functions where we crucially exploit submodularity and data structures to obtain fast, i.e. sublinear time subgradient updates. . The latter is crucial for beating the $n^{2}$ bound as we show that algorithms which access only subgradients of the Lovasz extension, and these include the theoretically best algorithms mentioned above, must make $\Omega(n)$ subgradient calls (even for functions whose range is $\{-1,0,1\}$).

Connected through hyperedge HE-1 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms"):
  with members:
    - "smaller cuts higher lower bounds"
    - "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "tight bounds on the round complexity of the distributed maximum coverage problem"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"

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"
    - "submodular optimization under noise"
    - "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 "quantum and classical algorithms for approximate submodular function minimization"):
  with members:
    - "near optimal approximate discrete and continuous submodular function minimization"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "learning with submodular functions a convex optimization perspective"
    - "sublinear optimization for machine learning"

Connected through hyperedge HE-4 (induced by source paper "solving strong substitutes product mix auctions"):
  with members:
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "computing walrasian equilibria fast algorithms and structural properties"
    - "provable submodular minimization using wolfe s algorithm"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-5 (induced by source paper "robust budget allocation via continuous submodular functions"):
  with members:
    - "robust monotone submodular function maximization"
    - "submodular optimization under noise"
    - "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-6 (induced by source paper "gradient methods for submodular maximization"):
  with members:
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "a reduction for optimizing lattice submodular functions with diminishing returns"
    - "submodular functions from discrete to continous domains"
    - "sharp time data tradeoffs for linear inverse problems"
    - "near optimally teaching the crowd to classify"
    - "submodular optimization with submodular cover and submodular knapsack constraints"

Connected through hyperedge HE-7 (induced by source paper "submodular minimization under congruency constraints"):
  with members:
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "submodular approximation sampling based algorithms and lower bounds"

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)