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: "submodularity on hypergraphs from sets to sequences"
  Abstract: In a nutshell, submodular functions encode an intuitive notion of diminishing returns. As a result, submodularity appears in many important machine learning tasks such as feature selection and data summarization. Although there has been a large volume of work devoted to the study of submodular functions in recent years, the vast majority of this work has been focused on algorithms that output sets, not sequences. However, in many settings, the order in which we output items can be just as important as the items themselves. #R##N#To extend the notion of submodularity to sequences, we use a directed graph on the items where the edges encode the additional value of selecting items in a particular order. Existing theory is limited to the case where this underlying graph is a directed acyclic graph. In this paper, we introduce two new algorithms that provably give constant factor approximations for general graphs and hypergraphs having bounded in or out degrees. Furthermore, we show the utility of our new algorithms for real-world applications in movie recommendation, online link prediction, and the design of course sequences for MOOCs.

Connected through hyperedge HE-1 (induced by source paper "submodular maximization through barrier functions"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "adaptive sequence submodularity"
    - "data summarization at scale a two stage submodular approach"
    - "do less get more streaming submodular maximization with subsampling"
    - "distributed submodular maximization"
    - "constrained non monotone submodular maximization offline and secretary algorithms"

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 "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"):
  with members:
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "revisiting decomposable submodular function minimization with incidence relations"
    - "hypergcn a new method of training graph convolutional networks on hypergraphs"
    - "quadratic decomposable submodular function minimization"
    - "inhomogoenous hypergraph clustering with applications"
    - "cheeger inequalities for submodular transformations"
    - "computing minimum cuts in hypergraphs"
    - "higher order organization of complex 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.DS(Data Structures and Algorithms)