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: "logical rule induction and theory learning using neural theorem proving"
  Abstract: A hallmark of human cognition is the ability to continually acquire and distill observations of the world into meaningful, predictive theories. In this paper we present a new mechanism for logical theory acquisition which takes a set of observed facts and learns to extract from them a set of logical rules and a small set of core facts which together entail the observations. Our approach is neuro-symbolic in the sense that the rule pred- icates and core facts are given dense vector representations. The rules are applied to the core facts using a soft unification procedure to infer additional facts. After k steps of forward inference, the consequences are compared to the initial observations and the rules and core facts are then encouraged towards representations that more faithfully generate the observations through inference. Our approach is based on a novel neural forward-chaining differentiable rule induction network. The rules are interpretable and learned compositionally from their predicates, which may be invented. We demonstrate the efficacy of our approach on a variety of ILP rule induction and domain theory learning datasets.

Connected through hyperedge HE-1 (induced by source paper "neural logic machines"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "deepproblog neural probabilistic logic programming"
    - "tunneling neural perception and logic reasoning through abductive learning"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "learning explanatory rules from noisy data"
    - "robustfill neural program learning under noisy i o"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "learn to explain efficiently via neural logic inductive learning"):
  with members:
    - "neural logic machines"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "explaining explanations in ai"
    - "tucker tensor factorization for knowledge graph completion"
    - "inductive logic programming via differentiable deep neural logic networks"
    - "iterative visual reasoning beyond convolutions"
    - "towards neural theorem proving at scale"

Connected through hyperedge HE-3 (induced by source paper "unsupervised grounding of plannable first order logic representation from images"):
  with members:
    - "photo realistic blocksworld dataset"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "deepproblog neural probabilistic logic programming"
    - "relational deep reinforcement learning"
    - "relational inductive biases deep learning and graph networks"
    - "a simple neural network module for relational reasoning"
    - "end to end differentiable proving"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"

Connected through hyperedge HE-4 (induced by source paper "neural theorem provers do not learn rules without exploration"):
  with members:
    - "neural logic machines"
    - "a comprehensive survey on graph neural networks"
    - "deepproblog neural probabilistic logic programming"
    - "measuring abstract reasoning in neural networks"
    - "towards neural theorem proving at scale"
    - "learning explanatory rules from noisy data"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "end to end differentiable proving"

Connected through hyperedge HE-5 (induced by source paper "sld resolution reduction of second order horn fragments technical report"):
  with members:
    - "exploiting answer set programming with external sources for meta interpretive learning"
    - "learning explanatory rules from noisy data"
    - "end to end differentiable proving"
    - "deep learning for ontology reasoning"
    - "minimization for generalized boolean formulas"
    - "redundancy deduction schemes and minimum size bases for association rules"
    - "redundancy deduction schemes and minimum size bases for association rules"
    - "redundancy in logic i cnf propositional formulae"

Connected through hyperedge HE-6 (induced by source paper "rudas synthetic datasets for rule learning and evaluation tools"):
  with members:
    - "neural logic machines"
    - "differentiable reasoning on large knowledge bases and natural language"
    - "deepproblog neural probabilistic logic programming"
    - "towards neural theorem proving at scale"
    - "learning explanatory rules from noisy data"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "rdf2rules learning rules from rdf knowledge bases by mining frequent predicate cycles"

Connected through hyperedge HE-7 (induced by source paper "logical reduction of metarules"):
  with members:
    - "inductive general game playing"
    - "exploiting answer set programming with external sources for meta interpretive learning"
    - "learning explanatory rules from noisy data"
    - "minimization for generalized boolean formulas"
    - "redundancy in logic i cnf propositional formulae"
    - "redundancy in logic ii 2cnf and horn propositional formulae"

Connected through hyperedge HE-8 (induced by source paper "feed forward neural networks need inductive bias to learn equality relations"):
  with members:
    - "extrapolation in nlp"
    - "relational inductive bias for physical construction in humans and machines"
    - "a simple neural network module for relational reasoning"
    - "prototypical networks for few shot learning"
    - "adam a method for stochastic optimization"

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)