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: "the fastest and shortest algorithm for all well defined problems"
  Abstract: An algorithm $M$ is described that solves any well-defined problem $p$ as quickly as the fastest algorithm computing a solution to $p$, save for a factor of 5 and low-order additive terms. $M$ optimally distributes resources between the execution of provably correct $p$-solving programs and an enumeration of all proofs, including relevant proofs of program correctness and of time bounds on program runtimes. $M$ avoids Blum's speed-up theorem by ignoring programs without correctness proof. $M$ has broader applicability and can be faster than Levin's universal search, the fastest method for inverting functions save for a large multiplicative constant. An extension of Kolmogorov complexity and two novel natural measures of function complexity are used to show that the most efficient program computing some function $f$ is also among the shortest programs provably computing $f$.

Connected through hyperedge HE-1 (induced by source paper "a theory of incremental compression"):
  with members:
    - "universal intelligence a definition of machine intelligence"
    - "optimal ordered problem solver"

Connected through hyperedge HE-2 (induced by source paper "arguments for the effectiveness of human problem solving"):
  with members:
    - "optimal ordered problem solver"

Connected through hyperedge HE-3 (induced by source paper "the sp theory of intelligence distinctive features and advantages"):
  with members:
    - "semantic amodal segmentation"
    - "deep learning in neural networks"
    - "proposal for the creation of a research facility for the development of the sp machine"
    - "text understanding from scratch"
    - "sketch a net that beats humans"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "neural turing machines"
    - "application of the sp theory of intelligence to the understanding of natural vision and the development of computer vision"

Connected through hyperedge HE-4 (induced by source paper "deep learning in neural networks"):
  with members:
    - "going deeper with convolutions"
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "a clockwork rnn"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "sequence to sequence learning with neural networks"
    - "multi column deep neural networks for offline handwritten chinese character classification"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "efficient visual coding from retina to v2"

Connected through hyperedge HE-5 (induced by source paper "ai evaluation past present and future"):
  with members:
    - "definition and properties to assess multi agent environments as social intelligence tests"
    - "the arcade learning environment an evaluation platform for general agents"
    - "no free lunch versus occam s razor in supervised learning"
    - "an approximation of the universal intelligence measure"
    - "universal algorithmic intelligence a mathematical top down approach"
    - "tests of machine intelligence"
    - "universal intelligence a definition of machine intelligence"
    - "graphics turing test"

Connected through hyperedge HE-6 (induced by source paper "one decade of universal artificial intelligence"):
  with members:
    - "can intelligence explode"
    - "principles of solomonoff induction and aixi"
    - "asymptotically optimal agents"
    - "universal learning theory"
    - "an approximation of the universal intelligence measure"
    - "axioms for rational reinforcement learning"
    - "non equivalence of universal priors"
    - "feature reinforcement learning in practice"

Connected through hyperedge HE-7 (induced by source paper "cognitive bias for universal algorithmic intelligence"):
  with members:
    - "the new ai general sound relevant for physics"

Connected through hyperedge HE-8 (induced by source paper "powerplay training an increasingly general problem solver by continually searching for the simplest still unsolvable problem"):
  with members:
    - "first experiments with powerplay"
    - "self delimiting neural networks"
    - "planning to be surprised optimal bayesian exploration in dynamic environments"
    - "optimal ordered problem solver"
    - "reinforcement learning a survey"

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.CC(Computational Complexity)