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: "fast pseudo random fingerprints"
  Abstract: We propose a method to exponentially speed up computation of various fingerprints, such as the ones used to compute similarity and rarity in massive data sets. Rather then maintaining the full stream of $b$ items of a universe $[u]$, such methods only maintain a concise fingerprint of the stream, and perform computations using the fingerprints. The computations are done approximately, and the required fingerprint size $k$ depends on the desired accuracy $\epsilon$ and confidence $\delta$. Our technique maintains a single bit per hash function, rather than a single integer, thus requiring a fingerprint of length $k = O(\frac{\ln \frac{1}{\delta}}{\epsilon^2})$ bits, rather than $O(\log u \cdot \frac{\ln \frac{1}{\delta}}{\epsilon^2})$ bits required by previous approaches. The main advantage of the fingerprints we propose is that rather than computing the fingerprint of a stream of $b$ items in time of $O(b \cdot k)$, we can compute it in time $O(b \log k)$. Thus this allows an exponential speedup for the fingerprint construction, or alternatively allows achieving a much higher accuracy while preserving computation time. Our methods rely on a specific family of pseudo-random hashes for which we can quickly locate hashes resulting in small values.

Connected through hyperedge HE-1 (induced by source paper "hyperminhash jaccard index sketching in loglog space"):
  with members:
    - "new cardinality estimation algorithms for hyperloglog sketches"
    - "new cardinality estimation methods for hyperloglog sketches"
    - "bottom k and priority sampling set similarity and subset sums with minimal independence"
    - "b bit minwise hashing"

Connected through hyperedge HE-2 (induced by source paper "hyperminhash minhash in loglog space"):
  with members:
    - "new cardinality estimation algorithms for hyperloglog sketches"
    - "new cardinality estimation methods for hyperloglog sketches"
    - "bottom k and priority sampling set similarity and subset sums with minimal independence"

Connected through hyperedge HE-3 (induced by source paper "more analysis of double hashing for balanced allocations"):
  with members:
    - "balanced allocations a simple proof for the heavily loaded case"
    - "on the k independence required by linear probing and minwise independence"
    - "balanced allocations and double hashing"

Connected through hyperedge HE-4 (induced by source paper "bottom k and priority sampling set similarity and subset sums with minimal independence"):
  with members:
    - "on the k independence required by linear probing and minwise independence"
    - "even better framework for min wise based algorithms"
    - "stream sampling for variance optimal estimation of subset sums"
    - "linear probing with constant independence"

Connected through hyperedge HE-5 (induced by source paper "balanced allocations and double hashing"):
  with members:
    - "the power of simple tabulation hashing"
    - "balanced allocation on graphs"

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)