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: "learning of human like algebraic reasoning using deep feedforward neural networks"
  Abstract: Abstract   Human-like rewriting, which is an algebraic reasoning system imitating human intelligence of problem solving, is proposed in this work. In order to imitate both learning and reasoning aspects of human cognition, a deep feedforward neural network learns from algebraic reasoning examples produced by humans and then uses learnt experiences to guide other reasoning processes. This work shows that the neural network can learn human’s behaviours of solving mathematical problems, and it can indicate suitable directions of reasoning, so that intelligent and heuristic reasoning can be performed. Moreover, human-like rewriting bridges the gap between symbolic reasoning and biologically inspired machine learning. To enable the neural network to recognise patterns of symbolic expressions with non-deterministic sizes, the expressions are reduced to partial tree representations and then vectorised as numeric features. Further, the centralisation method, symbolic association vectors and rule application records are used to improve the vectorised features. With these approaches, human-like rewriting shows satisfactory performance on the tasks of solving linear equations and computing derivations and indefinite integrals.

Connected through hyperedge HE-1 (induced by source paper "trainable back propagated functional transfer matrices"):
  with members:
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "deepmath deep sequence models for premise selection"
    - "deepcoder learning to write programs"

Connected through hyperedge HE-2 (induced by source paper "learning to perform local rewriting for combinatorial optimization"):
  with members:
    - "gamepad a learning environment for theorem proving"
    - "learning heuristics for automated reasoning through deep reinforcement learning"
    - "learning to optimize tensor programs"
    - "can neural networks understand logical entailment"
    - "learning combinatorial optimization algorithms over graphs"
    - "reinforcement learning with deep energy based policies"
    - "deep reinforcement learning for multi resource multi machine job scheduling"
    - "adaptive neural compilation"

Connected through hyperedge HE-3 (induced by source paper "premise selection with neural networks and distributed representation of features"):
  with members:
    - "premise selection for theorem proving by deep graph embedding"
    - "deepmath deep sequence models for premise selection"
    - "holophrasm a neural automated theorem prover for higher order logic"
    - "mizar 40 for mizar 40"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "from gameplay to symbolic reasoning learning sat solver heuristics in the style of alpha go zero"):
  with members:
    - "neural message passing for quantum chemistry"
    - "towards deep symbolic reinforcement learning"
    - "improved semantic representations from tree structured long short term memory networks"
    - "playing atari with deep reinforcement learning"

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.AI(Artificial Intelligence)