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: "constrained submodular maximization via a non symmetric technique"
  Abstract: The study of combinatorial optimization problems with a submodular objective has attracted much attention in recent years. Such problems are important in both theory and practice because their objective functions are very general. Obtaining further improvements for many submodular maximization problems boils down to finding better algorithms for optimizing a relaxation of them known as the multilinear extension. #R##N#In this work we present an algorithm for optimizing the multilinear relaxation whose guarantee improves over the guarantee of the best previous algorithm (which was given by Ene and Nguyen (2016)). Moreover, our algorithm is based on a new technique which is, arguably, simpler and more natural for the problem at hand. In a nutshell, previous algorithms for this problem rely on symmetry properties which are natural only in the absence of a constraint. Our technique avoids the need to resort to such properties, and thus, seems to be a better fit for constrained problems.

Connected through hyperedge HE-1 (induced by source paper "non monotone submodular maximization with nearly optimal adaptivity and query complexity"):
  with members:
    - "fast parallel algorithms for feature selection"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "submodular optimization in the mapreduce model"
    - "graph sketching against adaptive adversaries applied to the minimum degree algorithm"
    - "unconstrained submodular maximization with constant adaptive complexity"
    - "parallelizing greedy for submodular set function maximization in matroids and beyond"

Connected through hyperedge HE-2 (induced by source paper "interlaced greedy algorithm for maximization of submodular functions in nearly linear time"):
  with members:
    - "parallel algorithm for non monotone dr submodular maximization"
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "do less get more streaming submodular maximization with subsampling"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "constrained submodular maximization beyond 1 e"

Connected through hyperedge HE-3 (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"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "approximate supermodularity bounds for experimental design"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "constrained submodular maximization beyond 1 e"
    - "lazier than lazy greedy"
    - "efficient feature group sequencing for anytime linear prediction"

Connected through hyperedge HE-4 (induced by source paper "an improved algorithm for the submodular secretary problem with a cardinality constraint"):
  with members:
    - "combinatorial prophet inequalities"
    - "submodular secretary problems cardinality matching and linear constraints"
    - "building a good team secretary problems and the supermodular degree"
    - "the submodular secretary problem goes linear"
    - "the simulated greedy algorithm for several submodular matroid secretary problems"
    - "constrained non monotone submodular maximization offline and secretary algorithms"

Connected through hyperedge HE-5 (induced by source paper "nearly linear time deterministic algorithm for maximizing non monotone submodular functions under cardinality constraint"):
  with members:
    - "streaming non monotone submodular maximization personalized video summarization on the fly"

Connected through hyperedge HE-6 (induced by source paper "parallelizing greedy for submodular set function maximization in matroids and beyond"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "submodular optimization in the mapreduce model"
    - "an optimal approximation for submodular maximization under a matroid constraint in the adaptive complexity model"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"

Connected through hyperedge HE-7 (induced by source paper "non monotone submodular maximization with nearly optimal adaptivity complexity"):
  with members:
    - "fast parallel algorithms for feature selection"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "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"
    - "submodular optimization in the mapreduce model"
    - "graph sketching against adaptive adversaries applied to the minimum degree algorithm"
    - "unconstrained submodular maximization with constant adaptive complexity"

Connected through hyperedge HE-8 (induced by source paper "submodular maximization with matroid and packing constraints in parallel"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "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.DS(Data Structures and Algorithms)