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: "differentiable genetic programming"
  Abstract: We introduce the use of high order automatic differentiation, implemented via the algebra of truncated Taylor polynomials, in genetic programming. Using the Cartesian Genetic Programming encoding we obtain a high-order Taylor representation of the program output that is then used to back-propagate errors during learning. The resulting machine learning framework is called differentiable Cartesian Genetic Programming (dCGP). In the context of symbolic regression, dCGP offers a new approach to the long unsolved problem of constant representation in GP expressions. On several problems of increasing complexity we find that dCGP is able to find the exact form of the symbolic expression as well as the constants values. We also demonstrate the use of dCGP to solve a large class of differential equations and to find prime integrals of dynamical systems, presenting, in both cases, results that confirm the efficacy of our approach.

Connected through hyperedge HE-1 (induced by source paper "neural network architecture search with differentiable cartesian genetic programming for regression"):
  with members:
    - "eat nas elastic architecture transfer for accelerating large scale neural architecture search"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "the quest for the golden activation function"
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "efficient architecture search by network transformation"
    - "searching for activation functions"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"

Connected through hyperedge HE-2 (induced by source paper "data driven discovery of free form governing differential equations"):
  with members:
    - "weight agnostic neural networks"
    - "coarse scale pdes from fine scale observations via machine learning"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "machine learning of linear differential equations using gaussian processes"
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"

Connected through hyperedge HE-3 (induced by source paper "learning concise representations for regression by evolving networks of trees"):
  with members:
    - "a two step disentanglement method"
    - "where are we now a large benchmark study of recent symbolic regression methods"
    - "efficient neural architecture search via parameter sharing"
    - "image to image translation for cross domain disentanglement"
    - "progressive neural architecture search"
    - "large scale evolution of image classifiers"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"

Connected through hyperedge HE-4 (induced by source paper "positional cartesian genetic programming"):
  with members:
    - "linear combination of distance measures for surrogate models in genetic programming"
    - "evolving simple programs for playing atari games"

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.NE(Neural and Evolutionary Computing)