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 "regular partitions and their use in structural pattern recognition"):
  Abstract: Recent years are characterized by an unprecedented quantity of available network data which are produced at an astonishing rate by an heterogeneous variety of interconnected sensors and devices. This high-throughput generation calls for the development of new effective methods to store, retrieve, understand and process massive network data. In this thesis, we tackle this challenge by introducing a framework to summarize large graphs based on Szemeredi's Regularity Remma (RL), which roughly states that any sufficiently large graph can almost entirely be partitioned into a bounded number of random-like bipartite graphs. The partition resulting from the RL gives rise to a summary, which inherits many of the essential structural properties of the original graph. We first extend an heuristic version of the RL to improve its efficiency and its robustness. We use the proposed algorithm to address graph-based clustering and image segmentation tasks. In the second part of the thesis, we introduce a new heuristic algorithm which is characterized by an improvement of the summary quality both in terms of reconstruction error and of noise filtering. We use the proposed heuristic to address the graph search problem defined under a similarity measure. Finally, we study the linkage among the regularity lemma, the stochastic block model and the minimum description length. This study provide us a principled way to develop a graph decomposition algorithm based on stochastic block model which is fitted using likelihood maximization.

Includes members:
  Member "separating structure from noise in large graphs using the regularity lemma":
    also connected through other hyperedges:
      (none)
  Member "analysis of large sparse graphs using regular decomposition of graph distance matrices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "separating structure from noise in large graphs using the regularity lemma")
  Member "network summarization with preserved spectral properties":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "separating structure from noise in large graphs using the regularity lemma")
  Member "regular decomposition an information and graph theoretic approach to stochastic block models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analysis of large sparse graphs using regular decomposition of graph distance matrices")
  Member "on the interplay between strong regularity and graph densification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "separating structure from noise in large graphs using the regularity lemma")
  Member "fast edge detection using structured forests":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "saliency enhancement using gradient domain edges merging")
  Member "local correlation clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "query efficient correlation clustering")

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)