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: "identifying hierarchical structure in sequences a linear time algorithm"
  Abstract: SEQUITUR is an algorithm that infers a hierarchical structure from a sequence of discrete symbols by replacing repeated phrases with a grammatical rule that generates the phrase, and continuing this process recursively. The result is a hierarchical representation of the original sequence, which offers insights into its lexical structure. The algorithm is driven by two constraints that reduce the size of the grammar, and produce structure as a by-product. SEQUITUR breaks new ground by operating incrementally. Moreover, the method's simple structure permits a proof that it operates in space and time that is linear in the size of the input. Our implementation can process 50,000 symbols per second and has been applied to an extensive range of real world sequences.

Connected through hyperedge HE-1 (induced by source paper "cocopie making mobile ai sweet as pie compression compilation co design goes a long way"):
  with members:
    - "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "wide activation for efficient and accurate image super resolution"
    - "multi style generative network for real time transfer"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-2 (induced by source paper "ensemble grammar induction for detecting anomalies in time series"):
  with members:
    - "lscp locally selective combination in parallel outlier ensembles"

Connected through hyperedge HE-3 (induced by source paper "mining insights from weakly structured event data"):
  with members:
    - "discovering more precise process models from event logs by filtering out chaotic activities"
    - "interest driven discovery of local process models"
    - "the imprecisions of precision measures in process mining"
    - "unsupervised event abstraction using pattern abstraction and local process models"
    - "heuristic approaches for generating local process models through log projections"
    - "conformance checking based on multi perspective declarative process models"
    - "event abstraction for process mining using supervised learning techniques"
    - "mining process model descriptions of daily life through event abstraction"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning with structured hierarchical grammar representations of actions"):
  with members:
    - "hierarchical reinforcement learning via advantage weighted information maximization"
    - "dot to dot explainable hierarchical reinforcement learning for robotic manipulation"
    - "model based reinforcement learning for atari"
    - "relational deep reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "soft actor critic algorithms and applications"
    - "learning to repeat fine grained action repetition for deep reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

Connected through hyperedge HE-5 (induced by source paper "rpair rescaling repair with rsync"):
  with members:
    - "gract a grammar based compressed index for trajectory data"
    - "mr repair grammar compression based on maximal repeats"
    - "prefix free parsing for building big bwts"
    - "time space trade offs for lempel ziv compressed indexing"
    - "compressed indexing with signature grammars"
    - "universal indexes for highly repetitive document collections"
    - "dynamic index lz factorization and lce queries in compressed space"
    - "a really simple approximation of smallest grammar"

Connected through hyperedge HE-6 (induced by source paper "entropy bounds for grammar based tree compressors"):
  with members:
    - "grammar based compression of unranked trees"
    - "using statistical encoding to achieve tree succinctness never seen before"
    - "entropy bounds for grammar compression"
    - "a universal tree balancing theorem"
    - "universal tree source coding using grammar based compression"
    - "a universal grammar based code for lossless compression of binary trees"
    - "large alphabets and incompressibility"

Connected through hyperedge HE-7 (induced by source paper "semantic rl with action grammars data efficient learning of hierarchical task abstractions"):
  with members:
    - "feudal networks for hierarchical reinforcement learning"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "the option critic architecture"
    - "towards deep symbolic reinforcement learning"
    - "lexis an optimization framework for discovering the hierarchical structure of sequential data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "the smallest grammar problem revisited"):
  with members:
    - "entropy bounds for grammar compression"
    - "at the roots of dictionary compression string attractors"
    - "a succinct grammar compression"
    - "approximation of grammar based compression via recompression"

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)