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: "a derandomized sparse johnson lindenstrauss transform"
  Abstract: Recent work of [Dasgupta-Kumar-Sarlos, STOC 2010] gave a sparse Johnson-Lindenstrauss transform and left as a main open question whether their construction could be efficiently derandomized. We answer their question affirmatively by giving an alternative proof of their result requiring only bounded independence hash functions. Furthermore, the sparsity bound obtained in our proof is improved. The main ingredient in our proof is a spectral moment bound for quadratic forms that was recently used in [Diakonikolas-Kane-Nelson, FOCS 2010].

Connected through hyperedge HE-1 (induced by source paper "distributed learning with sublinear communication"):
  with members:
    - "introduction to online convex optimization"
    - "space lower bounds for linear prediction"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "communication compression for decentralized training"
    - "detecting correlations with little memory and communication"
    - "distributed simulation and distributed inference"
    - "sparsified sgd with memory"
    - "inference under information constraints i lower bounds from chi square contraction"

Connected through hyperedge HE-2 (induced by source paper "derandomizing compressed sensing with combinatorial design"):
  with members:
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "low rank matrix recovery from clifford orbits"
    - "stable low rank matrix recovery via null space properties"
    - "a partial derandomization of phaselift using spherical designs"
    - "learning without concentration"
    - "low rank matrix recovery from rank one measurements"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-3 (induced by source paper "embeddings of schatten norms with applications to data streams"):
  with members:
    - "sketches for matrix norms faster smaller and more general"
    - "spectrum estimation from samples"
    - "on approximating functions of the singular values in a stream"
    - "streaming symmetric norms via measure concentration"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "principal component analysis and higher correlations for distributed data"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"

Connected through hyperedge HE-4 (induced by source paper "simple analysis of sparse sign consistent jl"):
  with members:
    - "a sparse johnson lindenstrauss transform"
    - "sparser johnson lindenstrauss transforms"
    - "graph sparsification by effective resistances"

Connected through hyperedge HE-5 (induced by source paper "deterministic parallel algorithms for bilinear objective functions"):
  with members:
    - "deterministic parallel algorithms for fooling polylogarithmic juntas and the lovasz local lemma"

Connected through hyperedge HE-6 (induced by source paper "isometric sketching of any set via the restricted isometry property"):
  with members:
    - "the restricted isometry property of subsampled fourier matrices"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "sharp time data tradeoffs for linear inverse problems"
    - "learning without concentration"
    - "randomized sketches of convex programs with sharp guarantees"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "fast and rip optimal transforms"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"

Connected through hyperedge HE-7 (induced by source paper "circulant matrices and differential privacy"):
  with members:
    - "restricted isometry property for general p norms"
    - "differentially private linear algebra in the streaming model"
    - "privacy via the johnson lindenstrauss transform"
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private data analysis of social networks via restricted sensitivity"
    - "the johnson lindenstrauss transform itself preserves differential privacy"
    - "beating randomized response on incoherent matrices"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"

Connected through hyperedge HE-8 (induced by source paper "weighted polynomial approximations limits for learning and pseudorandomness"):
  with members:
    - "pseudorandomness for concentration bounds and signed majorities"
    - "analysis of boolean functions"
    - "agnostic learning of disjunctions on symmetric distributions"
    - "approximate resilience monotonicity and the complexity of agnostic learning"
    - "pseudorandomness for regular branching programs via fourier analysis"
    - "the complexity of learning halfspaces using generalized linear methods"
    - "fooling functions of halfspaces under product distributions"
    - "bounded independence fools halfspaces"

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)