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: "self improving algorithms"
  Abstract: We investigate ways in which an algorithm can improve its expected performance by fine-tuning itself automatically with respect to an unknown input distribution $\mathcal{D}$. We assume here that $\mathcal{D}$ is of product type. More precisely, suppose that we need to process a sequence $I_1,I_2,\ldots$ of inputs $I=(x_1,x_2,\ldots,x_n)$ of some fixed length $n$, where each $x_i$ is drawn independently from some arbitrary, unknown distribution $\mathcal{D}_i$. The goal is to design an algorithm for these inputs so that eventually the expected running time will be optimal for the input distribution $\mathcal{D}=\prod_i\mathcal{D}_i$. We give such self-improving algorithms for two problems: (i) sorting a sequence of numbers and (ii) computing the Delaunay triangulation of a planar point set. Both algorithms achieve optimal expected limiting complexity. The algorithms begin with a training phase during which they collect information about the input distribution, followed by a stationary regime in which the algorithms settle to their optimized incarnations.

Connected through hyperedge HE-1 (induced by source paper "learned frequency estimation algorithms under zipfian distribution"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "learning to prune speeding up repeated computations"
    - "learning to branch"
    - "competitive caching with machine learned advice"
    - "the case for learned index structures"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "learning combinatorial optimization algorithms over graphs"
    - "a deep learning approach to structured signal recovery"

Connected through hyperedge HE-2 (induced by source paper "learning to prune speeding up repeated computations"):
  with members:
    - "learning to branch"
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "osmnx new methods for acquiring constructing analyzing and visualizing complex street networks"
    - "online optimization of smoothed piecewise constant functions"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "a pac approach to application specific algorithm selection"

Connected through hyperedge HE-3 (induced by source paper "instance optimal geometric algorithms"):
  with members:
    - "entropy triangulation and point location in planar subdivisions"
    - "odds on trees"
    - "biased range trees"
    - "optimal adaptive algorithms for finding the nearest and farthest point on a parametric black box curve"
    - "optimal aggregation algorithms for middleware"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"
    - "dense point sets have sparse delaunay triangulations"

Connected through hyperedge HE-4 (induced by source paper "from seed ai to technological singularity via recursively self improving software"):
  with members:
    - "the universe of minds"
    - "bounded recursive self improvement"
    - "construction of an np problem with an exponential lower bound"
    - "goedel machines self referential universal problem solvers making provably optimal self improvements"
    - "definition and complexity of some basic metareasoning problems"
    - "optimal ordered problem solver"

Connected through hyperedge HE-5 (induced by source paper "unions of onions preprocessing imprecise points for fast onion decomposition"):
  with members:
    - "halving balls in deterministic linear time"
    - "convex hull of points lying on lines in o nlogn time after preprocessing"

Connected through hyperedge HE-6 (induced by source paper "beyond worst case analysis for joins with minesweeper"):
  with members:
    - "size bounds and query plans for relational joins"
    - "instance optimal geometric algorithms"
    - "leapfrog triejoin a worst case optimal join algorithm"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "tight lower bounds for query processing on streaming and external memory data"
    - "hypertree decompositions and tractable queries"
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-7 (induced by source paper "a static optimality transformation with applications to planar point location"):
  with members:
    - "entropy triangulation and point location in planar subdivisions"
    - "improved upper bounds for pairing heaps"
    - "point location in disconnected planar subdivisions"
    - "odds on trees"
    - "layered working set trees"

Connected through hyperedge HE-8 (induced by source paper "self improving algorithms for coordinate wise maxima and convex hulls"):
  with members:
    - "instance optimal geometric algorithms"
    - "convex hull of points lying on lines in o nlogn time after preprocessing"
    - "convex hull of imprecise points in o n log n time after preprocessing"
    - "odds on trees"

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)