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: "unifying the landscape of cell probe lower bounds"
  Abstract: We show that a large fraction of the data-structure lower bounds known today in fact follow by reduction from the communication complexity of lopsided (asymmetric) set disjointness. This includes lower bounds for: #R##N#* high-dimensional problems, where the goal is to show large space lower bounds. #R##N#* constant-dimensional geometric problems, where the goal is to bound the query time for space O(n polylog n). #R##N#* dynamic problems, where we are looking for a trade-off between query and update time. (In this case, our bounds are slightly weaker than the originals, losing a lglg n factor.) #R##N#Our reductions also imply the following new results: #R##N#* an Omega(lg n / lglg n) bound for 4-dimensional range reporting, given space O(n polylog n). This is quite timely, since a recent result solved 3D reporting in O(lglg n) time, raising the prospect that higher dimensions could also be easy. #R##N#* a tight space lower bound for the partial match problem, for constant query time. #R##N#* the first lower bound for reachability oracles. #R##N#In the process, we prove optimal randomized lower bounds for lopsided set disjointness.

Connected through hyperedge HE-1 (induced by source paper "equivalence of systematic linear data structures and matrix rigidity"):
  with members:
    - "static data structure lower bounds imply rigidity"
    - "matrix rigidity and the croot lev pach lemma"
    - "crossing the logarithmic barrier for dynamic boolean data structure lower bounds"
    - "probabilistic rank and matrix rigidity"
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"
    - "new unconditional hardness results for dynamic and online problems"
    - "adapt or die polynomial lower bounds for non adaptive dynamic data structures"
    - "min rank conjecture for log depth circuits"

Connected through hyperedge HE-2 (induced by source paper "lower bounds on time space trade offs for approximate near neighbors"):
  with members:
    - "randomized approximate nearest neighbor search with limited adaptivity"
    - "simple average case lower bounds for approximate near neighbor from isoperimetric inequalities"
    - "exploiting the structure stochastic gradient methods using raw clusters"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "scalability and total recall with fast coveringlsh"
    - "learning to hash for indexing big data a survey"
    - "a faster subquadratic algorithm for finding outlier correlations"
    - "tight lower bounds for data dependent locality sensitive hashing"

Connected through hyperedge HE-3 (induced by source paper "optimal hashing based time space trade offs for approximate near neighbors"):
  with members:
    - "scalability and total recall with fast coveringlsh"

Connected through hyperedge HE-4 (induced by source paper "planar reachability in linear space and constant time"):
  with members:
    - "linear space approximate distance oracles for planar bounded genus and minor free graphs"
    - "exact distance oracles for planar graphs"
    - "maintaining information in fully dynamic trees with top trees"

Connected through hyperedge HE-5 (induced by source paper "optimal planar orthogonal skyline counting queries"):
  with members:
    - "sorted range reporting"
    - "orthogonal range searching on the ram revisited"
    - "optimal succinctness for range minimum queries"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-6 (induced by source paper "information cost tradeoffs for augmented index and streaming language recognition"):
  with members:
    - "lower bounds for sparse recovery"
    - "the space complexity of recognizing well parenthesized expressions in the streaming model the index function revisited"
    - "recognizing well parenthesized expressions in the streaming model"

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)