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 "tensor prediction rademacher complexity and random 3 xor"):
  Abstract: Here we study the tensor prediction problem, where the goal is to accurately predict the entries of a low rank, third-order tensor (with noise) given as few observations as possible. We give algorithms based on the sixth level of the sum-of-squares hierarchy that work with roughly $m = n^{3/2}$observations, and we complement our result by showing that any attempt to solve tensor prediction with $m = n^{3/2 - \epsilon}$ observations through the sum-of-squares hierarchy needs $n^{2 \epsilon}$ rounds and consequently would run in moderately exponential time. In contrast, information theoretically roughly $m = n$ observations suffice. #R##N#This work is part of a broader agenda of studying computational vs. statistical tradeoffs through the sum-of-squares hierarchy. In particular, for linear inverse problems (such as tensor prediction) the natural sum-of-squares relaxation gives rise to a sequence of norms. Our approach is to characterize their Rademacher complexity. Moreover, both our upper and lower bounds are based on connections between this, and the task of strongly refuting random $3$-XOR formulas, and the resolution proof system.

Includes members:
  Member "how to refute a random csp":
    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 "tight lower bounds for planted clique in the degree 4 sos program":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "average case lower bounds for learning sparse mixtures robust estimation and semirandom adversaries")
  Member "sos and planted clique tight analysis of mpw moments at all degrees and an optimal lower bound at degree four":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a nearly tight sum of squares lower bound for the planted clique problem")
  Member "dictionary learning and tensor decomposition via the sum of squares method":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "sum of squares proofs and the quest toward optimal algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sum of squares bounds for the ordering principle")
  Member "rounding sum of squares relaxations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "smoothed analysis of tensor decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improper learning for non stochastic control")
  Member "from average case complexity to improper learning complexity":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "time space tradeoffs for distinguishing distributions and applications to security of goldreich s prg")

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)