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: "optimal succinct rank data structure via approximate nonnegative tensor decomposition"
  Abstract: Given an $n$-bit array $A$, the succinct rank data structure problem asks to construct a data structure using space $n+r$ bits for $r\ll n$, supporting rank queries of form $\mathtt{rank}(x)=\sum_{i=0}^{x-1} A[i]$. In this paper, we design a new succinct rank data structure with $r=n/(\log n)^{\Omega(t)}+n^{1-c}$ and query time $O(t)$ for some constant $c>0$, improving the previous best-known by Patrascu [Pat08], which has $r=n/(\frac{\log n}{t})^{\Omega(t)}+\tilde{O}(n^{3/4})$ bits of redundancy. For $r>n^{1-c}$, our space-time tradeoff matches the cell-probe lower bound by Patrascu and Viola [PV10], which asserts that $r$ must be at least $n/(\log n)^{O(t)}$. Moreover, one can avoid an $n^{1-c}$-bit lookup table when the data structure is implemented in the cell-probe model, achieving $r=\lceil n/(\log n)^{\Omega(t)}\rceil$. It matches the lower bound for the full range of parameters. #R##N#En route to our new data structure design, we establish an interesting connection between succinct data structures and approximate nonnegative tensor decomposition. Our connection shows that for specific problems, to construct a space-efficient data structure, it suffices to approximate a particular tensor by a sum of (few) nonnegative rank-$1$ tensors. For the rank problem, we explicitly construct such an approximation, which yields an explicit construction of the data structure.

Connected through hyperedge HE-1 (induced by source paper "nearly optimal static las vegas succinct dictionary"):
  with members:
    - "how to store a random walk"
    - "more haste less waste lowering the redundancy in fully indexable dictionaries"
    - "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)