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 "dynamic automatic differentiation of gpu broadcast kernels"):
  Abstract: We show how forward-mode automatic differentiation (AD) can be employed within larger reverse-mode computations to dynamically differentiate broadcast operations in a GPU-friendly manner. Our technique fully exploits the broadcast Jacobian's inherent sparsity structure, and unlike a pure reverse-mode approach, this "mixed-mode" approach does not require a backwards pass over the broadcasted operation's subgraph, obviating the need for several reverse-mode-specific programmability restrictions on user-authored broadcast operations. Most notably, this approach allows broadcast fusion in primal code despite the presence of data-dependent control flow. We discuss an experiment in which a Julia implementation of our technique outperformed pure reverse-mode TensorFlow and Julia implementations for differentiating through broadcast operations within an HM-LSTM cell update calculation.

Includes members:
  Member "confusion of tagged perturbations in forward automatic differentiation of higher order functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the versatility of open logical relations continuity automatic differentiation and a containment theorem")
  Member "effective extensible programming unleashing julia on gpus":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "differentiate everything with a reversible programming language")
  Member "don t unroll adjoint differentiating ssa form programs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "differentiate everything with a reversible programming language")
  Member "automatic full compilation of julia programs and ml models to cloud tpus":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "demystifying differentiable programming shift reset the penultimate backpropagator":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a differential form pullback programming language for higher order reverse mode automatic differentiation")
  Member "the simple essence of automatic differentiation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a differential form pullback programming language for higher order reverse mode automatic differentiation")
  Member "tangent automatic differentiation using source code transformation in python":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "differentiable visual computing")
  Member "forward mode automatic differentiation in julia":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "differentiate everything with a reversible programming language")

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.MS(Mathematical Software)