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: "parallel peeling algorithms"
  Abstract: The analysis of several algorithms and data structures can be framed as a peeling process on a random hypergraph: vertices with degree less than k are removed until there are no vertices of degree less than k left. The remaining hypergraph is known as the k-core. In this paper, we analyze parallel peeling processes, where in each round, all vertices of degree less than k are removed. It is known that, below a specific edge density threshold, the k-core is empty with high probability. We show that, with high probability, below this threshold, only (log log n)/log(k-1)(r-1) + O(1) rounds of peeling are needed to obtain the empty k-core for r-uniform hypergraphs. Interestingly, we show that above this threshold, Omega(log n) rounds of peeling are required to find the non-empty k-core. Since most algorithms and data structures aim to peel to an empty k-core, this asymmetry appears fortunate. We verify the theoretical results both with simulation and with a parallel implementation using graphics processing units (GPUs). Our implementation provides insights into how to structure parallel peeling algorithms for efficiency in practice.

Connected through hyperedge HE-1 (induced by source paper "local algorithms for hierarchical dense subgraph discovery"):
  with members:
    - "shared memory graph truss decomposition"
    - "estimation of graphlet statistics"
    - "static graph challenge subgraph isomorphism"
    - "higher order organization of complex networks"
    - "locally estimating core numbers"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "graph cluster randomization network exposure to multiple universes"
    - "truss decomposition in massive networks"

Connected through hyperedge HE-2 (induced by source paper "robust set reconciliation via locality sensitive hashing"):
  with members:
    - "reconciling graphs and sets of sets"
    - "wear minimization for cuckoo hashing how not to throw a lot of eggs into one basket"
    - "simple multi party set reconciliation"
    - "invertible bloom lookup tables"
    - "straggler identification in round trip data streams via newton s identities and invertible bloom filters"

Connected through hyperedge HE-3 (induced by source paper "a simple hash class with strong randomness properties in graphs and hypergraphs"):
  with members:
    - "from independence to expansion and back again"
    - "approximately minwise independence with twisted tabulation"
    - "simple tabulation fast expanders double tabulation and high independence"
    - "on the k independence required by linear probing and minwise independence"
    - "independence of tabulation based hash classes"
    - "on the insertion time of cuckoo hashing"
    - "tight thresholds for cuckoo hashing via xorsat"

Connected through hyperedge HE-4 (induced by source paper "a novel approach to finding near cliques the triangle densest subgraph problem"):
  with members:
    - "counting small cliques in mapreduce"
    - "mathematical and algorithmic analysis of network and biological data"
    - "a second look at counting triangles in graph streams revised"
    - "decompositions of triangle dense graphs"
    - "how hard is counting triangles in the streaming model"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "densest subgraph in streaming and mapreduce"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"

Connected through hyperedge HE-5 (induced by source paper "cache oblivious peeling of random hypergraphs"):
  with members:
    - "fast prefix search in little space with applications"
    - "biff bloom filter codes fast error correction for large data sets"
    - "invertible bloom lookup tables"
    - "tight thresholds for cuckoo hashing via xorsat"
    - "bloomier filters a second look"

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)