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: "deepproblog neural probabilistic logic programming"
  Abstract: We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques can be adapted for the new language. Our experiments demonstrate that DeepProbLog supports both symbolic and subsymbolic representations and inference, 1) program induction, 2) probabilistic (logic) programming, and 3) (deep) learning from examples. To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples.

Connected through hyperedge HE-1 (induced by source paper "analyzing differentiable fuzzy logic operators"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "semi supervised learning using differentiable reasoning"
    - "injecting prior knowledge for transfer learning into reinforcement learning algorithms using logic tensor networks"
    - "learning and t norms theory"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "large scale gan training for high fidelity natural image synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep learning a critical appraisal"

Connected through hyperedge HE-2 (induced by source paper "graph neural networks meet neural symbolic computing a survey and perspective"):
  with members:
    - "the next decade in ai four steps towards robust artificial intelligence"
    - "run csp unsupervised learning of message passing networks for binary constraint satisfaction problems"
    - "recurrent independent mechanisms"
    - "deep learning for symbolic mathematics"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "neural symbolic computing an effective methodology for principled integration of machine learning and reasoning"
    - "the use of deep learning for symbolic integration a review of lample and charton 2019"
    - "learning to reason leveraging neural networks for approximate dnf counting"

Connected through hyperedge HE-3 (induced by source paper "relational neural machines"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "neural logic machines"
    - "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"
    - "on a convex logic fragment for learning and reasoning"
    - "differentiable learning of logical rules for knowledge base reasoning"

Connected through hyperedge HE-4 (induced by source paper "from statistical relational to neuro symbolic artificial intelligence"):
  with members:
    - "relational neural machines"
    - "neural logic 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"

Connected through hyperedge HE-5 (induced by source paper "teaching the old dog new tricks supervised learning with constraints"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "a convex framework for fair regression"
    - "end to end differentiable proving"
    - "equality of opportunity in supervised learning"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "a confidence based approach for balancing fairness and accuracy"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "efficient piecewise training of deep structured models for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "injecting domain knowledge in neural networks a controlled experiment on a constrained problem"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "semi supervised learning using differentiable reasoning"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "deep neural networks as 0 1 mixed integer linear programs a feasibility study"
    - "learning explanatory rules from noisy data"
    - "neural combinatorial optimization with reinforcement learning"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"

Connected through hyperedge HE-7 (induced by source paper "neural logic machines"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "logical rule induction and theory learning using neural theorem proving"
    - "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-8 (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"
    - "relational deep reinforcement learning"
    - "logical rule induction and theory learning using neural theorem proving"
    - "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"

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)