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: "tensorlog a differentiable deductive database"
  Abstract: Large knowledge bases (KBs) are useful in many tasks, but it is unclear how to integrate this sort of knowledge into "deep" gradient-based learning systems. To address this problem, we describe a probabilistic deductive database, called TensorLog, in which reasoning uses a differentiable process. In TensorLog, each clause in a logical theory is first converted into certain type of factor graph. Then, for each type of query to the factor graph, the message-passing steps required to perform belief propagation (BP) are "unrolled" into a function, which is differentiable. We show that these functions can be composed recursively to perform inference in non-trivial logical theories containing multiple interrelated clauses and predicates. Both compilation and inference in TensorLog are efficient: compilation is linear in theory size and proof depth, and inference is linear in database size and the number of message-passing steps used in BP. We also present experimental results with TensorLog and discuss its relationship to other first-order probabilistic logics.

Connected through hyperedge HE-1 (induced by source paper "inductive relation prediction by subgraph reasoning"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "clutrr a diagnostic benchmark for inductive reasoning from text"
    - "link prediction based on graph neural networks"
    - "logic attention based neighborhood aggregation for inductive knowledge graph embedding"
    - "how powerful are graph neural networks"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"

Connected through hyperedge HE-2 (induced by source paper "neural consciousness flow"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "relational deep reinforcement learning"
    - "learning a sat solver from single bit supervision"
    - "graph networks as learnable physics engines for inference and control"
    - "relational recurrent neural networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-3 (induced by source paper "dynamically pruned message passing networks for large scale knowledge graph reasoning"):
  with members:
    - "relational recurrent neural networks"
    - "relational inductive biases deep learning and graph networks"
    - "variational knowledge graph reasoning"
    - "how powerful are graph neural networks"
    - "m walk learning to walk over graphs using monte carlo tree search"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "non local neural networks"

Connected through hyperedge HE-4 (induced by source paper "nlprolog reasoning with weak unification for question answering in natural language"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "weaver deep co encoding of questions and documents for machine reading"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "neural models for reasoning over multiple mentions using coreference"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "a survey on open information extraction"
    - "learning explanatory rules from noisy data"

Connected through hyperedge HE-5 (induced by source paper "learning invariants through soft unification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "an attentive survey of attention models"
    - "learning explanatory rules from noisy data"
    - "explanation in artificial intelligence insights from the social sciences"
    - "end to end differentiable proving"
    - "harnessing deep neural networks with logic rules"
    - "tracking the world state with recurrent entity networks"
    - "the mythos of model interpretability"

Connected through hyperedge HE-6 (induced by source paper "drum end to end differentiable rule mining on knowledge graphs"):
  with members:
    - "variational graph recurrent neural networks"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "semi implicit graph variational auto encoders"
    - "tucker tensor factorization for knowledge graph completion"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "graph embedding techniques applications and performance a survey"
    - "towards neural theorem proving at scale"

Connected through hyperedge HE-7 (induced by source paper "distilling structured knowledge into embeddings for explainable and accurate recommendation"):
  with members:
    - "neural ir meets graph embedding a ranking model for product search"
    - "jointly learning explainable rules for recommendation with knowledge graph"
    - "deep learning based recommender system a survey and new perspectives"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "explainable recommendation a survey and new perspectives"
    - "neural factorization machines for sparse predictive analytics"
    - "deep mutual learning"
    - "neural collaborative filtering"

Connected through hyperedge HE-8 (induced by source paper "strong equivalence for lpmln programs"):
  with members:
    - "weight learning in a probabilistic extension of answer set programs"
    - "a probabilistic extension of action language bc"
    - "computing lpmln using asp and mln solvers"
    - "logic tensor networks for semantic image interpretation"
    - "probabilistic reasoning with answer sets"
    - "propositional theories are strongly equivalent to logic programs"
    - "diagnostic reasoning with a prolog"

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)