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: "iterative learning of answer set programs from context dependent examples"
  Abstract: In recent years, several frameworks and systems have been proposed that extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis together with a given background knowledge. In existing systems, the background knowledge is the same for all examples; however, examples may be context-dependent. This means that some examples should be explained in the context of some information, whereas others should be explained in different contexts. In this paper, we capture this notion and present a context-dependent extension of the Learning from Ordered Answer Sets framework. In this extension, contexts can be used to further structure the background knowledge. We then propose a new iterative algorithm, ILASP2i, which exploits this feature to scale up the existing ILASP2 system to learning tasks with large numbers of examples. We demonstrate the gain in scalability by applying both algorithms to various learning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2i system can be two orders of magnitude faster and use two orders of magnitude less memory, whilst preserving the same average accuracy. This paper is under consideration for acceptance in TPLP.

Connected through hyperedge HE-1 (induced by source paper "making sense of sensory input"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "model based reinforcement learning for atari"
    - "unsupervised discovery of parts structure and dynamics"
    - "composable planning with attributes"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "graph networks as learnable physics engines for inference and control"
    - "semi supervised online structure learning for composite event recognition"
    - "flexible neural representation for physics prediction"

Connected through hyperedge HE-2 (induced by source paper "a logic based framework leveraging neural networks for studying the evolution of neurological disorders"):
  with members:
    - "the seventh answer set programming competition design and results"
    - "the external interface for extending wasp"
    - "asp core 2 input language format"
    - "a comprehensive survey on graph neural networks"
    - "interpreting cnns via decision trees"
    - "constraint answer set programming without grounding"
    - "automated verification of neural networks advances challenges and perspectives"
    - "visual interpretability for deep learning a survey"

Connected through hyperedge HE-3 (induced by source paper "inductive general game playing"):
  with members:
    - "inductive learning of answer set programs from noisy examples"
    - "exploiting answer set programming with external sources for meta interpretive learning"
    - "learning explanatory rules from noisy data"
    - "learning first order definable concepts over structures of small degree"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "structure learning of probabilistic logic programs by searching the clause space"
    - "learning weak constraints in answer set programming"
    - "clingo asp control preliminary report"

Connected through hyperedge HE-4 (induced by source paper "induction of subgoal automata for reinforcement learning"):
  with members:
    - "joint inference of reward machines and policies for reinforcement learning"
    - "semantic rl with action grammars data efficient learning of hierarchical task abstractions"
    - "representing formal languages a comparison between finite automata and recurrent neural networks"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "symmetry breaking answer set solving"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-5 (induced by source paper "incremental and iterative learning of answer set programs from mutually distinct examples"):
  with members:
    - "best effort inductive logic programming via fine grained cost based hypothesis generation"
    - "distributed online learning of event definitions"
    - "improving scalability of inductive logic programming via pruning and best effort optimisation"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "learning weak constraints in answer set programming"
    - "incremental learning of event definitions with inductive logic programming"

Connected through hyperedge HE-6 (induced by source paper "inductive learning of answer set programs from noisy examples"):
  with members:
    - "learning explanatory rules from noisy data"
    - "improving scalability of inductive logic programming via pruning and best effort optimisation"
    - "learning weak constraints in answer set programming"
    - "introduction to the conll 2000 shared task chunking"

Connected through hyperedge HE-7 (induced by source paper "sketched answer set programming"):
  with members:
    - "jsketch sketching for java"
    - "predicate logic as a modeling language modeling and solving some machine learning and data mining problems with idp3"
    - "predicate logic as a modelling language the idp system"
    - "towards automated integration of guess and check programs in answer set programming a meta interpreter and applications"

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)