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 "data dimension reduction and network sparsification based on minimal algorithmic information loss"):
  Abstract: We introduce a family of unsupervised, domain-free, and (asymptotically) model-independent algorithms based on the principles of algorithmic information theory designed to minimize the loss of algorithmic information. The method coarse-grains data in an algorithmic fashion by collapsing regions that can be procedurally regenerated from the compressed version. We show that the method can preserve the salient properties of objects and structures in the process of data dimension reduction and denoising. Using suboptimal approximations of efficient (polynomial) estimations to algorithmic complexity by recent numerical methods of algorithmic probability we demonstrate how these algorithms can preserve structure properties, outperforming other algorithms in e.g. the area of network dimension reduction. As a case study, we report that the method preserves all the graph-theoretic indices measured on a well-known set of synthetic and real-world networks of very different nature, ranging from degree distribution and clustering coefficient to edge betweenness and degree and eigenvector centralities, achieving equal or significantly better results than other data reduction and some of the leading network sparsification methods.

Includes members:
  Member "coding theorem like behaviour and emergence of the universal distribution from resource bounded algorithmic probability":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithmic probability guided supervised machine learning on non differentiable spaces")
  Member "low algorithmic complexity entropy deceiving graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the thermodynamics of network coding and an algorithmic refinement of the principle of maximum entropy")
  Member "correspondence and independence of numerical evaluations of algorithmic information measures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "algorithmic data analytics small data matters and correlation versus causation")
  Member "graph sparsification by effective resistances":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "a model of internet topology using k shell decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "target privacy preserving for social networks")
  Member "the google similarity distance":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semantic relatedness for keyword disambiguation exploiting different embeddings")
  Member "the similarity metric":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "max margin class imbalanced learning with gaussian affinity")

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)