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: "opus an efficient admissible algorithm for unordered search"
  Abstract: OPUS is a branch and bound search algorithm that enables efficient admissible search through spaces for which the order of search operator application is not significant. The algorithm's search efficiency is demonstrated with respect to very large machine learning search spaces. The use of admissible search is of potential value to the machine learning community as it means that the exact learning biases to be employed for complex learning tasks can be precisely specified and manipulated. OPUS also has potential for application in other areas of artificial intelligence, notably, truth maintenance. This paper describes the OPUS (Optimized Pruning for Unordered Search) family of search algorithms. These algorithms provide efficient admissible search of search spaces in which the order of application of search operators is not significant. This search efficiency is achieved by the use of branch and bound techniques that employ domain specific pruning rules to provide a tightly focused traversal of the search space. While these algorithms have wide applicability, both within and beyond the scope of artificial intelligence, this paper focuses on their application in classification learning. Of particular significance, it is demonstrated that the algorithms can efficiently process many common classification learning problems. This contrasts with the seemingly widespread assumption that the sizes of the search spaces involved in machine learning require the use of heuristic search. The use of admissible search is of potential value in machine learning as it enables better experimental evaluation of alternative learning biases. Search is used in machine learning in an attempt to uncover classifiers that satisfy a learning bias. When heuristic search is used it is difficult to determine whether the search technique introduces additional implicit biases that cannot be properly identified. Such implicit biases may confound experimental results. In contrast, if admissible search is employed the experimenter can be assured that

Connected through hyperedge HE-1 (induced by source paper "lormika local rule based model interpretability with k optimal associations"):
  with members:
    - "local rule based explanations of black box decision systems"
    - "magix model agnostic globally interpretable explanations"
    - "a unified approach to interpreting model predictions"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-2 (induced by source paper "a bayesian approach to rule mining"):
  with members:
    - "fast algorithms for mining interesting frequent itemsets without minimum support"

Connected through hyperedge HE-3 (induced by source paper "discovering reliable dependencies from data hardness and improved algorithms"):
  with members:
    - "discovering reliable approximate functional dependencies"
    - "guarantees for greedy maximization of non submodular functions with applications"

Connected through hyperedge HE-4 (induced by source paper "skopus mining top k sequential patterns under leverage"):
  with members:
    - "ranking episodes using a partition model"
    - "discovering episodes with compact minimal windows"
    - "the long and the short of it summarising event sequences with serial episodes"
    - "summarizing data succinctly with the most informative itemsets"
    - "using background knowledge to rank itemsets"
    - "significance of episodes based on minimal windows"

Connected through hyperedge HE-5 (induced by source paper "real valued all dimensions search low overhead rapid searching over subsets of attributes"):
  with members:
    - "cached sufficient statistics for efficient machine learning with large datasets"

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)