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: "space constrained interval selection"
  Abstract: We study streaming algorithms for the interval selection problem: finding a maximum cardinality subset of disjoint intervals on the line. A deterministic 2-approximation streaming algorithm for this problem is developed, together with an algorithm for the special case of proper intervals, achieving improved approximation ratio of 3/2. We complement these upper bounds by proving that they are essentially best possible in the streaming setting: it is shown that an approximation ratio of $2 - \epsilon$ (or $3 / 2 - \epsilon$ for proper intervals) cannot be achieved unless the space is linear in the input size. In passing, we also answer an open question of Adler and Azar \cite{AdlerAzar03} regarding the space complexity of constant-competitive randomized preemptive online algorithms for the same problem.

Connected through hyperedge HE-1 (induced by source paper "weighted maximum independent set of geometric objects in turnstile streams"):
  with members:
    - "optimal streaming and tracking distinct elements with high probability"
    - "independent set size approximation in graph streams"
    - "on approximating maximum independent set of rectangles"
    - "maximum matching in turnstile streams"
    - "interval selection in the streaming model"

Connected through hyperedge HE-2 (induced by source paper "on the streaming complexity of fundamental geometric problems"):
  with members:
    - "optimal quantile approximation in streams"
    - "communication complexity for algorithm designers"
    - "interval selection in the streaming model"
    - "deterministic sampling and range counting in geometric data streams"

Connected through hyperedge HE-3 (induced by source paper "independent sets in vertex arrival streams"):
  with members:
    - "correlation clustering in data streams"
    - "interval selection in the streaming model"

Connected through hyperedge HE-4 (induced by source paper "independent set size approximation in graph streams"):
  with members:
    - "on estimating maximum matching size in graph streams"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "the sparse awakens streaming algorithms for matching size estimation in sparse graphs"
    - "sublinear time estimation of degree distribution moments the arboricity connection"
    - "universal sketches for the frequency negative moments and other decreasing streaming sums"

Connected through hyperedge HE-5 (induced by source paper "semi streaming set cover"):
  with members:
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming set cover problem"
    - "incidence geometries and the pass complexity of semi streaming set cover"
    - "streaming lower bounds for approximating max cut"
    - "single pass spectral sparsification in dynamic streams"
    - "maximum matching in semi streaming with few passes"
    - "improved approximation guarantees for weighted matching in the semi streaming model"
    - "graph sparsification in the semi streaming model"

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)