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 "the committee machine computational to statistical gaps in learning a two layers neural network"):
  Abstract: Heuristic tools from statistical physics have been used in the past to locate the phase transitions and compute the optimal learning and generalization errors in the teacher-student scenario in multi-layer neural networks. In this contribution, we provide a rigorous justification of these approaches for a two-layers neural network model called the committee machine. We also introduce a version of the approximate message passing (AMP) algorithm for the committee machine that allows to perform optimal learning in polynomial time for a large set of parameters. We find that there are regimes in which a low generalization error is information-theoretically achievable while the AMP algorithm fails to deliver it, strongly suggesting that no efficient algorithm exists for those cases, and unveiling a large computational gap.

Includes members:
  Member "overlap matrix concentration in optimal bayesian inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "concentration of the matrix valued minimum mean square error in optimal bayesian inference")
  Member "optimal errors and phase transitions in high dimensional generalized linear models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "mutual information for low rank even order symmetric tensor factorization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "overlap matrix concentration in optimal bayesian inference")
  Member "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "predicting trends in the quality of state of the art neural networks without access to training or testing data")
  Member "approximate message passing decoder and capacity achieving sparse superposition codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "decoding from pooled data phase transitions of message passing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "group testing with runlength constraints for topological molecular storage")
  Member "performance limits for noisy multimeasurement vector problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploiting prior information in block sparse signals")
  Member "spurious local minima are common in two layer relu neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")

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)