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: "guarantees for greedy maximization of non submodular functions with applications"
  Abstract: We investigate the performance of the standard Greedy algorithm for cardinality constrained maximization of non-submodular nondecreasing set functions. While there are strong theoretical guarantees on the performance of Greedy for maximizing submodular functions, there are few guarantees for non-submodular ones. However, Greedy enjoys strong empirical performance for many important non-submodular functions, e.g., the Bayesian A-optimality objective in experimental design. We prove theoretical guarantees supporting the empirical performance. Our guarantees are characterized by a combination of the (generalized) curvature $\alpha$ and the submodularity ratio $\gamma$. In particular, we prove that Greedy enjoys a tight approximation guarantee of $\frac{1}{\alpha}(1- e^{-\gamma\alpha})$ for cardinality constrained maximization. In addition, we bound the submodularity ratio and curvature for several important real-world objectives, including the Bayesian A-optimality objective, the determinantal function of a square submatrix and certain linear programs with combinatorial constraints. We experimentally validate our theoretical findings for both synthetic and real-world applications.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "beyond submodular maximization"):
  with members:
    - "distributed maximization of submodular plus diversity functions for multi label feature selection on huge datasets"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "local search for max sum diversification"
    - "max sum diversification monotone submodular functions and semi metric spaces"
    - "max sum diversity via convex programming"
    - "building a good team secretary problems and the supermodular degree"
    - "a new framework for distributed submodular maximization"

Connected through hyperedge HE-3 (induced by source paper "fast parallel algorithms for feature selection"):
  with members:
    - "non monotone submodular maximization with nearly optimal adaptivity and query complexity"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "the well tempered lasso"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "submodular maximization with matroid and packing constraints in parallel"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "unconstrained submodular maximization with constant adaptive complexity"

Connected through hyperedge HE-4 (induced by source paper "bayesian experimental design using regularized determinantal point processes"):
  with members:
    - "distributed estimation of the inverse hessian by determinantal averaging"
    - "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"
    - "correcting the bias in least squares regression with volume rescaled sampling"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "reverse iterative volume sampling for linear regression"
    - "leveraged volume sampling for linear regression"
    - "lectures on randomized numerical linear algebra"

Connected through hyperedge HE-5 (induced by source paper "submodular maximization beyond non negativity guarantees fast algorithms and applications"):
  with members:
    - "fast maximization of non submodular monotonic functions on the integer lattice"
    - "guess free maximization of submodular and linear sums"
    - "approximate supermodularity bounds for experimental design"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "constrained submodular maximization via a non symmetric technique"
    - "constrained submodular maximization beyond 1 e"
    - "lazier than lazy greedy"
    - "efficient feature group sequencing for anytime linear prediction"

Connected through hyperedge HE-6 (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"
    - "scalable bicriteria algorithms for the threshold activation problem in online social networks"
    - "submodular optimization under noise"
    - "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-7 (induced by source paper "non monotone submodular maximization with multiple knapsacks in static and dynamic settings"):
  with members:
    - "optimization of chance constrained submodular functions"
    - "pareto optimization for subset selection with dynamic cost constraints"
    - "greedy maximization of functions with bounded curvature under partition matroid constraints"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "max sum diversification monotone submodular functions and dynamic updates"
    - "maximizing non monotone submodular set functions subject to different constraints combined algorithms"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"

Connected through hyperedge HE-8 (induced by source paper "stochastic greedy closing the optimality gap in exact weak submodular maximization"):
  with members:
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "a randomized greedy algorithm for near optimal sensor scheduling in large scale sensor networks"
    - "lazier than lazy greedy"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "active semi supervised learning using submodular functions"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "subspace pursuit for compressive sensing signal reconstruction"

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.DM(Discrete Mathematics)