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: "an optimal bloom filter replacement based on matrix solving"
  Abstract: We suggest a method for holding a dictionary data structure, which maps keys to values, in the spirit of Bloom Filters. The space requirements of the dictionary we suggest are much smaller than those of a hashtable. We allow storing n keys, each mapped to value which is a string of k bits. Our suggested method requires nk + o(n) bits space to store the dictionary, and O(n) time to produce the data structure, and allows answering a membership query in O(1) memory probes. The dictionary size does not depend on the size of the keys. However, reducing the space requirements of the data structure comes at a certain cost. Our dictionary has a small probability of a one sided error. When attempting to obtain the value for a key that is stored in the dictionary we always get the correct answer. However, when testing for membership of an element that is not stored in the dictionary, we may get an incorrect answer, and when requesting the value of such an element we may get a certain random value. Our method is based on solving equations in GF(2^k) and using several hash functions. Another significant advantage of our suggested method is that we do not require using sophisticated hash functions. We only require pairwise independent hash functions. We also suggest a data structure that requires only nk bits space, has O(n2) preprocessing time, and has a O(log n) query time. However, this data structures requires a uniform hash functions. In order replace a Bloom Filter of n elements with an error proability of 2^{-k}, we require nk + o(n) memory bits, O(1) query time, O(n) preprocessing time, and only pairwise independent hash function. Even the most advanced previously known Bloom Filter would require nk+O(n) space, and a uniform hash functions, so our method is significantly less space consuming especially when k is small.

Connected through hyperedge HE-1 (induced by source paper "fully dynamic space efficient dictionaries and filters with constant number of memory accesses"):
  with members:
    - "bloom filters adaptivity and the dictionary problem"
    - "adaptive cuckoo filters"
    - "fast scalable construction of minimal perfect hash functions"
    - "dynamic integer sets with optimal rank select and predecessor search"
    - "how to approximate a set without knowing its size in advance"
    - "don t thrash how to cache your hash on flash"
    - "the power of simple tabulation hashing"
    - "backyard cuckoo hashing constant worst case operations with a succinct representation"

Connected through hyperedge HE-2 (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"
    - "bloomier filters a second look"

Connected through hyperedge HE-3 (induced by source paper "fast prefix search in little space with applications"):
  with members:
    - "bloomier filters a second look"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-4 (induced by source paper "bloom filters adaptivity and the dictionary problem"):
  with members:
    - "adaptive cuckoo filters"
    - "fast and powerful hashing using tabulation"
    - "bloom filters in adversarial environments"
    - "how robust are linear sketches to adaptive inputs"
    - "don t thrash how to cache your hash on flash"
    - "backyard cuckoo hashing constant worst case operations with a succinct representation"

Connected through hyperedge HE-5 (induced by source paper "compression for quadratic similarity queries"):
  with members:
    - "compression for quadratic similarity queries finite blocklength and practical schemes"
    - "the minimal compression rate for similarity identification"

Connected through hyperedge HE-6 (induced by source paper "how to approximate a set without knowing its size in advance"):
  with members:
    - "sliding bloom filters"
    - "backyard cuckoo hashing constant worst case operations with a succinct representation"
    - "de amortized cuckoo hashing provable worst case performance and experimental results"
    - "succinct data structures for retrieval and approximate membership"

Connected through hyperedge HE-7 (induced by source paper "the minimal compression rate for similarity identification"):
  with members:
    - "compression for quadratic similarity queries"
    - "fixed length lossy compression in the finite blocklength regime"
    - "the dispersion of lossy source coding"
    - "alphabet sizes of auxiliary random variables in canonical inner bounds"

Connected through hyperedge HE-8 (induced by source paper "various improvements to text fingerprinting"):
  with members:
    - "bloomier filters a second look"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

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)