human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "computing classic closeness centrality at scale"):
  Abstract: Closeness centrality, first considered by Bavelas (1948), is an importance measure of a node in a network which is based on the distances from the node to all other nodes. The classic definition, proposed by Bavelas (1950), Beauchamp (1965), and Sabidussi (1966), is (the inverse of) the average distance to all other nodes.   We propose the first highly scalable (near linear-time processing and linear space overhead) algorithm for estimating, within a small relative error, the classic closeness centralities of all nodes in the graph. Our algorithm applies to undirected graphs, as well as for centrality computed with respect to round-trip distances in directed graphs.   For directed graphs, we also propose an efficient algorithm that approximates generalizations of classic closeness centrality to outbound and inbound centralities. Although it does not provide worst-case theoretical approximation guarantees, it is designed to perform well on real networks.   We perform extensive experiments on large networks, demonstrating high scalability and accuracy.

Includes members:
  Member "all distances sketches revisited hip estimators for massive graphs analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sampling sketches for concave sublinear functions of frequencies")
  Member "axioms for centrality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "maximizing influence based group shapley centrality")
  Member "in core computation of geometric centralities with hyperball a hundred billion nodes and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "computing top k closeness centrality faster in unweighted graphs")
  Member "defining and evaluating network communities based on ground truth":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")
  Member "four degrees of separation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "diffusion improves graph learning")
  Member "robustness of social networks comparative results based on distance distributions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "all distances sketches revisited hip estimators for massive graphs analysis")
  Member "hyperanf approximating the neighbourhood function of very large graphs on a budget":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics")
  Member "layered label propagation a multiresolution coordinate free ordering for compressing social networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "2ps high quality edge partitioning with two phase streaming")

Question: Please tell me which class the source paper 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)