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: "lifted graphical models a survey"
  Abstract: This article presents a survey of work on lifted graphical models. We review a general form for a lifted graphical model, a par-factor graph, and show how a number of existing statistical relational representations map to this formalism. We discuss inference algorithms, including lifted inference algorithms, that efficiently compute the answers to probabilistic queries. We also review work in learning lifted graphical models from data. It is our belief that the need for statistical relational models (whether it goes by that name or another) will grow in the coming decades, as we are inundated with data which is a mix of structured and unstructured, with entities and relations extracted in a noisy manner from text, and with the need to reason effectively with this data. We hope that this synthesis of ideas from many different research groups will provide an accessible starting point for new researchers in this expanding field.

Connected through hyperedge HE-1 (induced by source paper "deep adaptive semantic logic dasl compiling declarative knowledge into deep neural networks"):
  with members:
    - "augmenting neural networks with first order logic"
    - "neural theorem provers do not learn rules without exploration"
    - "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"
    - "semantically regularized logic graph embeddings"
    - "pytorch an imperative style high performance deep learning library"
    - "backpropagating through structured argmax using a spigot"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"

Connected through hyperedge HE-2 (induced by source paper "state space abstractions for probabilistic inference a systematic review"):
  with members:
    - "stochastic planning and lifted inference"
    - "domain recursion for lifted inference with existential quantifiers"
    - "contextual symmetries in probabilistic graphical models"
    - "weighted positive binary decision diagrams for exact probabilistic inference"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "lifted tree reweighted variational inference"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "symmetry aware marginal density estimation"

Connected through hyperedge HE-3 (induced by source paper "lifted marginal map inference"):
  with members:
    - "solving marginal map problems with np oracles and parity constraints"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "map complexity results and approximation methods"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"

Connected through hyperedge HE-4 (induced by source paper "model based exception mining for object relational data"):
  with members:
    - "deep reinforcement learning in ice hockey for context aware player evaluation"
    - "on the chi square and higher order chi distances for approximating f divergences"
    - "graph based anomaly detection and description a survey"
    - "outlier detection by logic programming"

Connected through hyperedge HE-5 (induced by source paper "tensorlog deep learning meets probabilistic dbs"):
  with members:
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "harnessing deep neural networks with logic rules"
    - "key value memory networks for directly reading documents"
    - "learning to compose neural networks for question answering"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "approximation aware dependency parsing by belief propagation"
    - "lifted relational neural networks"

Connected through hyperedge HE-6 (induced by source paper "coarse to fine lifted map inference in computer vision"):
  with members:
    - "learning tractable probabilistic models for fault localization"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "rockit exploiting parallelism and symmetry for map inference in statistical relational models"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"

Connected through hyperedge HE-7 (induced by source paper "domain recursion for lifted inference with existential quantifiers"):
  with members:
    - "why is compiling lifted inference into a low level language so effective"
    - "automorphism groups of graphical models and lifted variational inference"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "probabilistic theorem proving"
    - "towards completely lifted search based probabilistic inference"

Connected through hyperedge HE-8 (induced by source paper "contextual symmetries in probabilistic graphical models"):
  with members:
    - "graph isomorphism in quasipolynomial time"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "rockit exploiting parallelism and symmetry for map inference in statistical relational models"
    - "automorphism groups of graphical models and lifted variational inference"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"

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)