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 weak constraints in answer set programming"
  Abstract: This paper contributes to the area of inductive logic programming by presenting a new learning framework that allows the learning of weak constraints in Answer Set Programming (ASP). The framework, called  Learning from Ordered Answer Sets , generalises our previous work on learning ASP programs without weak constraints, by considering a new notion of examples as  ordered  pairs of partial answer sets that exemplify which answer sets of a learned hypothesis (together with a given background knowledge) are  preferred  to others. In this new learning task inductive solutions are searched within a hypothesis space of normal rules, choice rules, and hard and weak constraints. We propose a new algorithm, ILASP2, which is sound and complete with respect to our new learning framework. We investigate its applicability to learning preferences in an interview scheduling problem and also demonstrate that when restricted to the task of learning ASP programs without weak constraints, ILASP2 can be much more efficient than our previously proposed system.

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"
    - "iterative learning of answer set programs from context dependent examples"
    - "structure learning of probabilistic logic programs by searching the clause space"
    - "clingo asp control preliminary report"

Connected through hyperedge HE-4 (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"
    - "iterative learning of answer set programs from context dependent examples"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "incremental learning of event definitions with inductive logic programming"

Connected through hyperedge HE-5 (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"
    - "iterative learning of answer set programs from context dependent examples"
    - "introduction to the conll 2000 shared task chunking"

Connected through hyperedge HE-6 (induced by source paper "improving scalability of inductive logic programming via pruning and best effort optimisation"):
  with members:
    - "iiscnlp at semeval 2016 task 2 interpretable sts with ilp based multiple chunk aligner"
    - "anytime answer set optimization via unsatisfiable core shrinking"
    - "modeling variations of first order horn abduction in answer set programming"
    - "incremental learning of event definitions with inductive logic programming"
    - "introduction to the conll 2000 shared task chunking"

Connected through hyperedge HE-7 (induced by source paper "iterative learning of answer set programs from context dependent examples"):
  with members:
    - "incremental learning of event definitions with inductive logic programming"

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)