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: "bloomier filters a second look"
  Abstract: A Bloom filter is a space efficient structure for storing static sets, where the space efficiency is gained at the expense of a small probability of false-positives. A Bloomier filter generalizes a Bloom filter to compactly store a function with a static support. In this article we give a simple construction of a Bloomier filter. The construction is linear in space and requires constant time to evaluate. The creation of our Bloomier filter takes linear time which is faster than the existing construction. We show how one can improve the space utilization further at the cost of increasing the time for creating the data structure.

Connected through hyperedge HE-1 (induced by source paper "peeling close to the orientability threshold spatial coupling in hashing based data structures"):
  with members:
    - "dense peelable random uniform hypergraphs"
    - "fast scalable construction of minimal perfect hash functions"
    - "bubing massive crawling for the masses"
    - "the multiple orientability thresholds for random hypergraphs"
    - "on the insertion time of random walk cuckoo hashing"
    - "high speed hashing for integers and strings"
    - "cache oblivious peeling of random hypergraphs"
    - "thresholds of spatially coupled systems via lyapunov s method"

Connected through hyperedge HE-2 (induced by source paper "dense peelable random uniform hypergraphs"):
  with members:
    - "fast scalable construction of minimal perfect hash functions"
    - "the multiple orientability thresholds for random hypergraphs"
    - "cache oblivious peeling of random hypergraphs"
    - "biff bloom filter codes fast error correction for large data sets"
    - "invertible bloom lookup tables"
    - "sharp load thresholds for cuckoo hashing"
    - "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 model for learned bloom filters and optimizing by sandwiching"):
  with members:
    - "optimizing learned bloom filters by sandwiching"
    - "a model for learned bloom filters and related structures"
    - "the case for learned index structures"
    - "adaptive cuckoo filters"
    - "bloom filters in adversarial environments"

Connected through hyperedge HE-4 (induced by source paper "xor filters faster and smaller than bloom and cuckoo filters"):
  with members:
    - "fast random integer generation in an interval"
    - "regular and almost universal hashing an efficient implementation"
    - "bloofi multidimensional bloom filters"
    - "an optimal bloom filter replacement based on matrix solving"

Connected through hyperedge HE-5 (induced by source paper "bloom filter variants for multiple sets a comparative assessment"):
  with members:
    - "optimizing bloom filter challenges solutions and comparisons"
    - "a shifting bloom filter framework for set queries"

Connected through hyperedge HE-6 (induced by source paper "matrix bloom filter an efficient probabilistic data structure for 2 tuple batch lookup"):
  with members:
    - "invertible bloom lookup tables"

Connected through hyperedge HE-7 (induced by source paper "fast prefix search in little space with applications"):
  with members:
    - "an optimal bloom filter replacement based on matrix solving"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-8 (induced by source paper "emoma exact match in one memory access"):
  with members:
    - "tight thresholds for cuckoo hashing via xorsat"

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)