human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "space lower bounds for linear prediction"):
  Abstract: We show that fundamental learning tasks, such as finding an approximate linear separator or linear regression, require memory at least \emph{quadratic} in the dimension, in a natural streaming setting. This implies that such problems cannot be solved (at least in this setting) by scalable memory-efficient streaming algorithms. Our results build on a memory lower bound for a simple linear-algebraic problem -- finding orthogonal vectors -- and utilize the estimates on the packing of the Grassmannian, the manifold of all linear subspaces of fixed dimension.

Includes members:
  Member "memory sample tradeoffs for linear regression with small error":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg")
  Member "testing matrix rank optimally":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "querying a matrix through matrix vector products")
  Member "extractor based time space lower bounds for learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg")
  Member "on communication complexity of classification problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the communication complexity of optimization")
  Member "matrix norms in data streams faster multi pass and row order":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension")
  Member "optimal approximate matrix product in terms of stable rank":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "speeding up linear programming using randomized linear algebra")
  Member "communication lower bounds for statistical estimation problems via a distributed data processing inequality":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "locally private hypothesis selection")
  Member "on the communication complexity of linear algebraic problems in the message passing model":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "tight bounds for the subspace sketch problem with applications")

Question: Please tell me which class the source paper 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.LG(Machine Learning)