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: "max sum diversification monotone submodular functions and dynamic updates"
  Abstract: Result diversification is an important aspect in web-based search, document summarization, facility location, portfolio management and other applications. Given a set of ranked results for a set of objects (e.g. web documents, facilities, etc.) with a distance between any pair, the goal is to select a subset $S$ satisfying the following three criteria: (a) the subset $S$ satisfies some constraint (e.g. bounded cardinality); (b) the subset contains results of high "quality"; and (c) the subset contains results that are "diverse" relative to the distance measure. The goal of result diversification is to produce a diversified subset while maintaining high quality as much as possible. We study a broad class of problems where the distances are a metric, where the constraint is given by independence in a matroid, where quality is determined by a monotone submodular function, and diversity is defined as the sum of distances between objects in $S$. Our problem is a generalization of the {\em max sum diversification} problem studied in \cite{GoSh09} which in turn is a generaliztion of the {\em max sum $p$-dispersion problem} studied extensively in location theory. It is NP-hard even with the triangle inequality. We propose two simple and natural algorithms: a greedy algorithm for a cardinality constraint and a local search algorithm for an arbitary matroid constraint. We prove that both algorithms achieve constant approximation ratios.

Connected through hyperedge HE-1 (induced by source paper "sequential evaluation and generation framework for combinatorial recommender system"):
  with members:
    - "deep reinforcement learning for page wise recommendations"
    - "off policy deep reinforcement learning without exploration"
    - "scalable agent alignment via reward modeling a research direction"
    - "virtual taobao virtualizing real world online retail environment for reinforcement learning"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "attention is all you need"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "learning combinatorial optimization algorithms over graphs"

Connected through hyperedge HE-2 (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"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "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-3 (induced by source paper "seq2slate re ranking and slate optimization with rnns"):
  with members:
    - "learning groupwise scoring functions using deep neural networks"
    - "universal transformers"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "learning a deep listwise context model for ranking refinement"
    - "top k off policy correction for a reinforce recommender system"
    - "learning permutations with sinkhorn policy gradient"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "almost optimal streaming algorithms for coverage problems"):
  with members:
    - "submodular optimization under noise"
    - "distributed coverage maximization via sketching"
    - "better streaming algorithms for the maximum coverage problem"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming set cover problem"
    - "kernelization via sampling with applications to dynamic graph streams"
    - "tight bounds for linear sketches of approximate matchings"
    - "randomized composable core sets for distributed submodular maximization"

Connected through hyperedge HE-5 (induced by source paper "local search for max sum diversification"):
  with members:
    - "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"
    - "max sum diversity via convex programming"
    - "randomized rounding for the largest simplex problem"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "maximum dispersion and geometric maximum weight cliques"

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)