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: "neural logic machines"
  Abstract: We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning. NLMs exploit the power of both neural networks---as function approximators, and logic programming---as a symbolic processor for objects with properties, relations, logic connectives, and quantifiers. After being trained on small-scale tasks (such as sorting short arrays), NLMs can recover lifted rules, and generalize to large-scale tasks (such as sorting longer arrays). In our experiments, NLMs achieve perfect generalization in a number of tasks, from relational reasoning tasks on the family tree and general graphs, to decision making tasks including sorting arrays, finding shortest paths, and playing the blocks world. Most of these tasks are hard to accomplish for neural networks or inductive logic programming alone.

Connected through hyperedge HE-1 (induced by source paper "relational neural machines"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "lyrics a general interface layer to integrate ai and deep learning"
    - "neural logic reinforcement learning"
    - "neural markov logic networks"
    - "learning relational representations with auto encoding logic programs"
    - "deepproblog neural probabilistic logic programming"
    - "on a convex logic fragment for learning and reasoning"
    - "differentiable learning of logical rules for knowledge base reasoning"

Connected through hyperedge HE-2 (induced by source paper "from statistical relational to neuro symbolic artificial intelligence"):
  with members:
    - "relational neural machines"
    - "differentiable reasoning on large knowledge bases and natural language"
    - "playgol learning programs through play"
    - "neural symbolic computing an effective methodology for principled integration of machine learning and reasoning"
    - "learning to reason leveraging neural networks for approximate dnf counting"
    - "synthesizing datalog programs using numerical relaxation"
    - "learning relational representations with auto encoding logic programs"
    - "deepproblog neural probabilistic logic programming"

Connected through hyperedge HE-3 (induced by source paper "meta reinforcement learning with autonomous inference of subtask dependencies"):
  with members:
    - "on first order meta learning algorithms"
    - "bayesian model agnostic meta learning"
    - "hierarchical reinforcement learning for zero shot generalization with subtask dependencies"
    - "meta reinforcement learning of structured exploration strategies"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "probabilistic model agnostic meta learning"
    - "learning explanatory rules from noisy data"
    - "a deep compositional framework for human like language acquisition in virtual environment"

Connected through hyperedge HE-4 (induced by source paper "turning 30 new ideas in inductive logic programming"):
  with members:
    - "learning higher order logic programs"
    - "on the measure of intelligence"
    - "logical reduction of metarules"
    - "forgetting to learn logic programs"
    - "playgol learning programs through play"
    - "synthesizing datalog programs using numerical relaxation"
    - "learning relational representations with auto encoding logic programs"
    - "inductive learning of answer set programs from noisy examples"

Connected through hyperedge HE-5 (induced by source paper "logic and the 2 simplicial transformer"):
  with members:
    - "universal transformers"
    - "generating long sequences with sparse transformers"
    - "an investigation of model free planning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "relational recurrent neural networks"
    - "generalisation of structural knowledge in the hippocampal entorhinal system"
    - "deep reinforcement learning that matters"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "learn to explain efficiently via neural logic inductive learning"):
  with members:
    - "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"
    - "logical rule induction and theory learning using neural theorem proving"
    - "towards neural theorem proving at scale"

Connected through hyperedge HE-7 (induced by source paper "graph representations for higher order logic and theorem proving"):
  with members:
    - "guiding high performance sat solvers with unsat core predictions"
    - "generating correctness proofs with neural networks"
    - "a comprehensive survey on graph neural networks"
    - "gamepad a learning environment for theorem proving"
    - "learning heuristics for automated reasoning through deep reinforcement learning"
    - "relational inductive biases deep learning and graph networks"
    - "can neural networks understand logical entailment"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-8 (induced by source paper "neural probabilistic logic programming in deepproblog"):
  with members:
    - "playgol learning programs through play"
    - "nlprolog reasoning with weak unification for question answering in natural language"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "learning relational representations with auto encoding logic programs"
    - "deepproblog neural probabilistic logic programming"
    - "neural guided constraint logic programming for program synthesis"
    - "learning explanatory rules from noisy data"
    - "a simple neural network module for relational reasoning"

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)