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: "simple tabulation fast expanders double tabulation and high independence"
  Abstract: Simple tabulation dates back to Zobrist in 1970 who used it for game playing programs. Keys are viewed as consisting of c characters from some alphabet Φ. We initialize c tables h0, ... , hc-1 mapping characters to random hash values. A key x = (x0, ... , xc-1) is hashed to h0[x0]⊕· · ·⊕hc-1[xc-1], where ⊕ denotes bit-wise exclusive-or. The scheme is extremely fast when the character hash tables hi are in cache. Simple tabulation hashing is not even 4-independent, but we show here that if we apply it twice, then we do get high independence. First we hash to some intermediate keys that are 6 times longer than the original keys, and then we hash the intermediate keys to the final hash values. The intermediate keys have d = 6c characters from Φ. We can then view the hash function as a highly unbalanced bipartite graph with keys on one side, each with edges to d output characters on the other side. We show that this graph has nice expansion properties, and from that it follows that if we perform another level of simple tabulation on the intermediate keys, then the composition is a highly independent hash function. More precisely, the independence we get is |Φ|Ω(1/c). In our O-notation, we view both |Φ| and c is going to infinity, but with c much smaller than |Φ|. Our space is O(c|Φ|) and the hash function is evaluated in O(c) time. Siegel [FOCS'89, SICOMP'04] has proved that with this space, if the hash function is evaluated in o(c) time, then the independence can only be o(c), so our evaluation time is best possible for Ω(c) independence-our independence is much higher if c = |Φ|o(1/c). Siegel used O(c)c evaluation time to get the same independence with similar space. Siegel's main focus was c = O(1), but we are exponentially faster when c = ω(1). Applying our scheme recursively, we can increase our independence to |Φ|Ω(1) with o(clogc) evaluation time. Compared with Siegel's scheme this is both faster and higher independence. Siegel states about his scheme that it is “far too slow for any practical application”. Our scheme is trivial to implement, and it does provide realistic implementations of 100-independent hashing for, say, 32-bit and 64-bit keys.

Connected through hyperedge HE-1 (induced by source paper "algorithms for similarity search and pseudorandomness"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "fast locality sensitive hashing for approximate near neighbor search"
    - "fast similarity sketching"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "optimal densification for fast and accurate minwise hashing"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "scalable and robust set similarity join"
    - "fast cross polytope locality sensitive hashing"

Connected through hyperedge HE-2 (induced by source paper "fast hashing with strong concentration bounds"):
  with members:
    - "optimal streaming and tracking distinct elements with high probability"
    - "faster 64 bit universal hashing using carry less multiplications"
    - "pseudorandomness via the discrete fourier transform"
    - "high speed hashing for integers and strings"
    - "from independence to expansion and back again"
    - "generating k independent variables in constant time"
    - "hashing for statistics over k partitions"
    - "bottom k and priority sampling set similarity and subset sums with minimal independence"

Connected through hyperedge HE-3 (induced by source paper "fully understanding the hashing trick"):
  with members:
    - "optimality of the johnson lindenstrauss lemma"
    - "hashing for statistics over k partitions"
    - "random projections for linear support vector machines"
    - "sparsity lower bounds for dimensionality reducing maps"
    - "a sparse johnson lindenstrauss transform"
    - "sparser johnson lindenstrauss transforms"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-4 (induced by source paper "non empty bins with simple tabulation hashing"):
  with members:
    - "fast and powerful hashing using tabulation"
    - "high speed hashing for integers and strings"
    - "hashing for statistics over k partitions"

Connected through hyperedge HE-5 (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"
    - "parallel peeling algorithms"
    - "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-6 (induced by source paper "fast and powerful hashing using tabulation"):
  with members:
    - "from independence to expansion and back again"
    - "the power of two choices with simple tabulation"
    - "generating k independent variables in constant time"
    - "approximately minwise independence with twisted tabulation"
    - "hashing for statistics over k partitions"
    - "on the k independence required by linear probing and minwise independence"
    - "invertible bloom lookup tables"
    - "ams without 4 wise independence on product domains"

Connected through hyperedge HE-7 (induced by source paper "quicksort largest bucket and min wise hashing with limited independence"):
  with members:
    - "from independence to expansion and back again"
    - "generating k independent variables in constant time"
    - "on the k independence required by linear probing and minwise independence"
    - "the power of simple tabulation hashing"
    - "linear probing with constant independence"

Connected through hyperedge HE-8 (induced by source paper "from independence to expansion and back again"):
  with members:
    - "generating k independent variables in constant time"
    - "dynamic integer sets with optimal rank select and predecessor search"

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)