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: "tight cell probe bounds for succinct boolean matrix vector multiplication"
  Abstract: The conjectured hardness of Boolean matrix-vector multiplication has been used with great success to prove conditional lower bounds for numerous important data structure problems, see Henzinger et al. [STOC'15]. In recent work, Larsen and Williams [SODA'17] attacked the problem from the upper bound side and gave a surprising cell probe data structure (that is, we only charge for memory accesses, while computation is free). Their cell probe data structure answers queries in $\tilde{O}(n^{7/4})$ time and is succinct in the sense that it stores the input matrix in read-only memory, plus an additional $\tilde{O}(n^{7/4})$ bits on the side. In this paper, we essentially settle the cell probe complexity of succinct Boolean matrix-vector multiplication. We present a new cell probe data structure with query time $\tilde{O}(n^{3/2})$ storing just $\tilde{O}(n^{3/2})$ bits on the side. We then complement our data structure with a lower bound showing that any data structure storing $r$ bits on the side, with $n < r < n^2$ must have query time $t$ satisfying $t r = \tilde{\Omega}(n^3)$. For $r \leq n$, any data structure must have $t = \tilde{\Omega}(n^2)$. Since lower bounds in the cell probe model also apply to classic word-RAM data structures, the lower bounds naturally carry over. We also prove similar lower bounds for matrix-vector multiplication over $\mathbb{F}_2$.

Connected through hyperedge HE-1 (induced by source paper "coarse grained complexity for dynamic algorithms"):
  with members:
    - "simple dynamic algorithms for maximal independent set and other problems"
    - "fully dynamic maximal independent set with sublinear update time"
    - "more consequences of falsifying seth and the orthogonal vectors conjecture"
    - "improved algorithms for fully dynamic maximal independent set"
    - "expander decomposition and pruning faster stronger and simpler"
    - "fully dynamic approximate maximum matching and minimum vertex cover in o log 3 n worst case update time"
    - "dynamic algorithms for graph coloring"
    - "crossing the logarithmic barrier for dynamic boolean data structure lower bounds"

Connected through hyperedge HE-2 (induced by source paper "dynamic matrix inverse improved algorithms and matching conditional lower bounds"):
  with members:
    - "dynamic approximate shortest paths and beyond subquadratic and worst case update time"
    - "a deterministic linear program solver in current matrix multiplication time"
    - "sensitive distance and reachability oracles for large batch updates"
    - "solving linear programs in the current matrix multiplication time"
    - "online bipartite matching with amortized o log 2 n replacements"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "dynamic minimum spanning forest with subpolynomial worst case update time"
    - "fully dynamic minimum spanning forest with improved worst case update time"

Connected through hyperedge HE-3 (induced by source paper "multi resolution hashing for fast pairwise summations"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "hashing based estimators for kernel density in high dimensions"
    - "on the fine grained complexity of empirical risk minimization kernel methods and neural networks"
    - "stochastic optimization with bandit sampling"
    - "fast amortized inference and learning in log linear models with randomly perturbed nearest neighbor search"
    - "distance sensitive hashing"
    - "optimal hashing based time space trade offs for approximate near neighbors"

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)