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: "succinct choice dictionaries"
  Abstract: The choice dictionary is introduced as a data structure that can be initialized with a parameter $n\in\mathbb{N}=\{1,2,\ldots\}$ and subsequently maintains an initially empty subset $S$ of $\{1,\ldots,n\}$ under insertion, deletion, membership queries and an operation choice that returns an arbitrary element of $S$. The choice dictionary appears to be fundamental in space-efficient computing. We show that there is a choice dictionary that can be initialized with $n$ and an additional parameter $t\in\mathbb{N}$ and subsequently occupies $n+O(n(t/w)^t+\log n)$ bits of memory and executes each of the four operations insert, delete, contains (i.e., a membership query) and choice in $O(t)$ time on a word RAM with a word length of $w=\Omega(\log n)$ bits. In particular, with $w=\Theta(\log n)$, we can support insert, delete, contains and choice in constant time using $n+O(n/(\log n)^t)$ bits for arbitrary fixed $t$. We extend our results to maintaining several pairwise disjoint subsets of $\{1,\ldots,n\}$. #R##N#We study additional space-efficient data structures for subsets $S$ of $\{1,\ldots,n\}$, including one that supports only insertion and an operation extract-choice that returns and deletes an arbitrary element of $S$. All our main data structures can be initialized in constant time and support efficient iteration over the set $S$, and we can allow changes to $S$ while an iteration over $S$ is in progress. We use these abilities crucially in designing the most space-efficient algorithms known for solving a number of graph and other combinatorial problems in linear time. In particular, given an undirected graph $G$ with $n$ vertices and $m$ edges, we can output a spanning forest of $G$ in $O(n+m)$ time with at most $(1+\epsilon)n$ bits of working memory for arbitrary fixed $\epsilon>0$.

Connected through hyperedge HE-1 (induced by source paper "space efficient vertex separators for treewidth"):
  with members:
    - "benchmarking treewidth as a practical component of tensor network simulations"
    - "space efficient dfs and applications simpler leaner faster"
    - "small uncolored and colored choice dictionaries"
    - "nearly optimal space efficient algorithm for depth first search"
    - "in place initializable arrays"
    - "rank select indices without tears"
    - "on the fly array initialization in less space"

Connected through hyperedge HE-2 (induced by source paper "linear time in place dfs and bfs on the word ram"):
  with members:
    - "small uncolored and colored choice dictionaries"
    - "space efficient biconnected components and recognition of outerplanar graphs"
    - "space time trade offs for stack based algorithms"
    - "radix sorting with no extra space"

Connected through hyperedge HE-3 (induced by source paper "extra space during initialization of succinct data structures and of dynamical initializable arrays"):
  with members:
    - "linear time in place dfs and bfs on the word ram"
    - "in place initializable arrays"
    - "frameworks for designing in place graph algorithms"
    - "succinct representations of permutations and functions"

Connected through hyperedge HE-4 (induced by source paper "space efficient dfs and applications simpler leaner faster"):
  with members:
    - "an optimal choice dictionary"
    - "rank select indices without tears"
    - "a simple test on 2 vertex and 2 edge connectivity"

Connected through hyperedge HE-5 (induced by source paper "fast breadth first search in still less space"):
  with members:
    - "small uncolored and colored choice dictionaries"
    - "in place initializable arrays"
    - "improved space efficient algorithms for bfs dfs and applications"

Connected through hyperedge HE-6 (induced by source paper "small uncolored and colored choice dictionaries"):
  with members:
    - "an optimal choice dictionary"
    - "in place initializable arrays"

Connected through hyperedge HE-7 (induced by source paper "frameworks for designing in place graph algorithms"):
  with members:
    - "biconnectivity st numbering and other applications of dfs using o n bits"
    - "canonizing graphs of bounded tree width in logspace"
    - "exact algorithms via monotone local search"
    - "selection from read only memory with limited workspace"
    - "the isomorphism problem for planar 3 connected graphs is in unambiguous logspace"
    - "radix sorting with no extra space"

Connected through hyperedge HE-8 (induced by source paper "an optimal choice dictionary"):
  with members:
    - "in place initializable arrays"

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)