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: "programming with personalized pagerank a locally groundable first order probabilistic logic"
  Abstract: In many probabilistic first-order representation systems, inference is performed by "grounding"---i.e., mapping it to a propositional representation, and then performing propositional inference. With a large database of facts, groundings can be very large, making inference and learning computationally expensive. Here we present a first-order probabilistic language which is well-suited to approximate "local" grounding: every query $Q$ can be approximately grounded with a small graph. The language is an extension of stochastic logic programs where inference is performed by a variant of personalized PageRank. Experimentally, we show that the approach performs well without weight learning on an entity resolution task; that supervised weight-learning improves accuracy; and that grounding time is independent of DB size. We also show that order-of-magnitude speedups are possible by parallelizing learning.

Connected through hyperedge HE-1 (induced by source paper "differentiable representations for multihop inference rules"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "neural compositional denotational semantics for question answering"
    - "deep sets"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "variational reasoning for question answering with knowledge graph"

Connected through hyperedge HE-2 (induced by source paper "learning tractable probabilistic models in open worlds"):
  with members:
    - "tractable querying and learning in hybrid domains via sum product networks"
    - "incremental knowledge base construction using deepdive"
    - "a differential approach to inference in bayesian networks"
    - "sum product networks a new deep architecture"
    - "solving sat and bayesian inference with backtracking search"

Connected through hyperedge HE-3 (induced by source paper "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"):
  with members:
    - "deep sets"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "end to end differentiable proving"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "reasonet learning to stop reading in machine comprehension"
    - "convolutional 2d knowledge graph embeddings"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "key value memory networks for directly reading documents"

Connected through hyperedge HE-4 (induced by source paper "differentiable learning of logical rules for knowledge base reasoning"):
  with members:
    - "key value memory networks for directly reading documents"
    - "learning a natural language interface with neural programmer"
    - "learning to compose neural networks for question answering"
    - "link prediction using embedded knowledge graphs"
    - "words or characters fine grained gating for reading comprehension"
    - "gated attention readers for text comprehension"
    - "tensorlog a differentiable deductive database"
    - "neural programmer inducing latent programs with gradient descent"

Connected through hyperedge HE-5 (induced by source paper "explainable entity based recommendations with knowledge graphs"):
  with members:
    - "do social explanations work studying and modeling the effects of social explanations in recommender systems"

Connected through hyperedge HE-6 (induced by source paper "learning multi faceted representations of individuals from heterogeneous evidence using neural networks"):
  with members:
    - "improved semantic representations from tree structured long short term memory networks"
    - "pte predictive text embedding through large scale heterogeneous text networks"
    - "line large scale information network embedding"
    - "traversing knowledge graphs in vector space"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"
    - "inferring user preferences by probabilistic logical reasoning over social networks"

Connected through hyperedge HE-7 (induced by source paper "heat kernel based community detection"):
  with members:
    - "flow based algorithms for local graph clustering"
    - "fast matrix computations for pairwise and columnwise commute times and katz scores"
    - "defining and evaluating network communities based on ground truth"
    - "implementing regularization implicitly via approximate eigenvector computation"
    - "google matrix dynamical attractors and ulam networks"
    - "fast incremental and personalized pagerank"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-8 (induced by source paper "inferring user preferences by probabilistic logical reasoning over social networks"):
  with members:
    - "timeline generation tracking individuals on twitter"
    - "discriminative probabilistic models for relational data"
    - "probabilistic similarity logic"
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

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)