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: "integrating learning and reasoning with deep logic models"
  Abstract: Deep learning is very effective at jointly learning feature representations and classification models, especially when dealing with high dimensional input patterns. Probabilistic logic reasoning, on the other hand, is capable to take consistent and robust decisions in complex environments. The integration of deep learning and logic reasoning is still an open-research problem and it is considered to be the key for the development of real intelligent agents. This paper presents Deep Logic Models, which are deep graphical models integrating deep learning and logic reasoning both for learning and inference. Deep Logic Models create an end-to-end differentiable architecture, where deep learners are embedded into a network implementing a continuous relaxation of the logic knowledge. The learning process allows to jointly learn the weights of the deep learners and the meta-parameters controlling the high-level reasoning. The experimental results show that the proposed methodology overtakes the limitations of the other approaches that have been proposed to bridge deep learning and reasoning.

Connected through hyperedge HE-1 (induced by source paper "analyzing differentiable fuzzy logic operators"):
  with members:
    - "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"
    - "deepproblog neural probabilistic logic programming"
    - "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 "relational neural machines"):
  with members:
    - "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"
    - "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-3 (induced by source paper "set structured latent representations"):
  with members:
    - "fspool learning set representations with featurewise sort pooling"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "deep set prediction networks"
    - "multi object representation learning with iterative variational inference"
    - "videograph recognizing minutes long human activities in videos"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "object based reasoning in vqa"
    - "deep sets"

Connected through hyperedge HE-4 (induced by source paper "teaching the old dog new tricks supervised learning with constraints"):
  with members:
    - "deepproblog neural probabilistic logic programming"
    - "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-5 (induced by source paper "injecting domain knowledge in neural networks a controlled experiment on a constrained problem"):
  with members:
    - "semi supervised learning using differentiable reasoning"
    - "deepproblog neural probabilistic logic programming"
    - "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-6 (induced by source paper "on incorporating semantic prior knowlegde in deep learning through embedding space constraints"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "actively seeking and learning from live data"
    - "hyperbolic neural networks"
    - "compositional attention networks for machine reasoning"
    - "deep learning a critical appraisal"
    - "visual question answering as a meta learning task"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "end to end differentiable proving"

Connected through hyperedge HE-7 (induced by source paper "neural markov logic networks"):
  with members:
    - "differentiable reasoning on large knowledge bases and natural language"
    - "lyrics a general interface layer to integrate ai and deep learning"
    - "deepproblog neural probabilistic logic programming"
    - "inference learning and population size projectivity for srl models"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "how powerful are graph neural networks"
    - "end to end differentiable proving"

Connected through hyperedge HE-8 (induced by source paper "learning and t norms theory"):
  with members:
    - "lyrics a general interface layer to integrate ai and deep learning"
    - "deepproblog neural probabilistic logic programming"
    - "on a convex logic fragment for learning and reasoning"
    - "logic tensor networks for semantic image interpretation"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "tensorflow a system for large scale machine learning"
    - "hinge loss markov random fields and probabilistic soft logic"

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.LG(Machine Learning)