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 "a more human way to play computer chess"):
  Abstract: The idea of dynamic move chains has been described in a preceding paper [13]. Re-using an earlier piece of search allows the tree to be forward-pruned, which is known to be dangerous, because that can remove new information which would only be realised through a more exhaustive search process. This paper has added to the forward-pruning technique by using 'Move Tables' that can act in the same way as Transposition Tables, but for moves not positions. They use an efficient memory structure and has put the design into the context of short and long-term memories. The long-term memory uses a play path with weight reinforcement, while the short-term memory can be immediately added or removed. Therefore, 'long branches' can play a short path, before returning to a full search at the resulting leaf nodes. Automatic feature analysis is now central to the search algorithm, where key squares and related squares can be generated automatically and used to guide the search process. Also using the analysis, if a search result is inferior, it can re-insert un-played moves that cover these key squares, thereby correcting those tactical errors. In particular, a type of move that the forward-pruning will fail on is recognised and a solution to that problem is suggested. This process reduces some gains made by forward-pruning but has created an intelligent framework that could make it more reliable. This has completed the theory of an earlier paper and resulted in a more human-like approach to searching for a chess move.

Includes members:
  Member "giraffe using deep reinforcement learning to play chess":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multiagent evaluation under incomplete information")
  Member "tree pruning for new search techniques in computer games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "new ideas for brain modelling 4")

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.AI(Artificial Intelligence)