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: "computing lpmln using asp and mln solvers"
  Abstract: LPMLN is a recent addition to probabilistic logic programming languages. Its main idea is to overcome the rigid nature of the stable model semantics by assigning a weight to each rule in a way similar to Markov Logic is defined. We present two implementations of LPMLN, $\text{LPMLN2ASP}$ and $\text{LPMLN2MLN}$. System $\text{LPMLN2ASP}$ translates LPMLN programs into the input language of answer set solver $\text{CLINGO}$, and using weak constraints and stable model enumeration, it can compute most probable stable models as well as exact conditional and marginal probabilities. System $\text{LPMLN2MLN}$ translates LPMLN programs into the input language of Markov Logic solvers, such as $\text{ALCHEMY}$, $\text{TUFFY}$, and $\text{ROCKIT}$, and allows for performing approximate probabilistic inference on LPMLN programs. We also demonstrate the usefulness of the LPMLN systems for computing other languages, such as ProbLog and Pearl's Causal Models, that are shown to be translatable into LPMLN. (Under consideration for acceptance in TPLP)

Connected through hyperedge HE-1 (induced by source paper "strong equivalence for lpmln programs"):
  with members:
    - "weight learning in a probabilistic extension of answer set programs"
    - "a probabilistic extension of action language bc"
    - "logic tensor networks for semantic image interpretation"
    - "tensorlog a differentiable deductive database"
    - "probabilistic reasoning with answer sets"
    - "propositional theories are strongly equivalent to logic programs"
    - "diagnostic reasoning with a prolog"

Connected through hyperedge HE-2 (induced by source paper "bridging commonsense reasoning and probabilistic planning via a probabilistic action language"):
  with members:
    - "sdrl interpretable and data efficient deep reinforcement learningleveraging symbolic planning"
    - "robot representing and reasoning with knowledge from reinforcement learning"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "a probabilistic extension of action language bc"
    - "integrating human provided information into belief state representation using dynamic factorization"
    - "integrating task motion planning with reinforcement learning for robust decision making in mobile robots"
    - "probabilistic reasoning with answer sets"

Connected through hyperedge HE-3 (induced by source paper "explainable fact checking with probabilistic answer set programming"):
  with members:
    - "claimrank detecting check worthy claims in arabic and english"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fever a large scale dataset for fact extraction and verification"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "finding streams in knowledge graphs to support fact checking"
    - "discriminative predicate path mining for fact checking in knowledge graphs"
    - "computational fact checking from knowledge networks"

Connected through hyperedge HE-4 (induced by source paper "on the strong equivalences for lpmln programs"):
  with members:
    - "asp core 2 input language format"
    - "weight learning in a probabilistic extension of answer set programs"
    - "probabilistic reasoning with answer sets"
    - "discovering classes of strongly equivalent logic programs"
    - "semantical characterizations and complexity of equivalences in answer set programming"
    - "logic programming with ordered disjunction"

Connected through hyperedge HE-5 (induced by source paper "elaboration tolerant representation of markov decision process via decision theoretic extension of probabilistic action language pbc"):
  with members:
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "a probabilistic extension of action language bc"
    - "answer set programming for non stationary markov decision processes"
    - "probabilistic reasoning with answer sets"

Connected through hyperedge HE-6 (induced by source paper "differentiable satisfiability and differentiable answer set programming for sampling based multi model optimization"):
  with members:
    - "weight learning in a probabilistic extension of answer set programs"
    - "learning a sat solver from single bit supervision"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "tensorlog a differentiable deductive database"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "distribution aware sampling and weighted model counting for sat"
    - "probabilistic inductive logic programming based on answer set programming"
    - "probabilistic reasoning with answer sets"

Connected through hyperedge HE-7 (induced by source paper "a probabilistic extension of action language bc"):
  with members:
    - "foundations for a probabilistic event calculus"
    - "probabilistic reasoning about actions in nonmonotonic causal theories"
    - "probabilistic reasoning with answer sets"
    - "diagnostic reasoning with a prolog"

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)