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: "embedding logical queries on knowledge graphs"
  Abstract: Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict "em what drugs are likely to target proteins involved with both diseases X and Y?" -- a query that requires reasoning about all possible proteins that {\em might} interact with diseases X and Y. Here we introduce a framework to efficiently make predictions about conjunctive logical queries -- a flexible but tractable subset of first-order logic -- on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a graph-based representation of social interactions derived from a popular web forum.

Connected through hyperedge HE-1 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base"):
  with members:
    - "generating logical forms from graph representations of text and entities"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "neural query language a knowledge base query language for tensorflow"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "neural compositional denotational semantics for question answering"
    - "improving text to sql evaluation methodology"
    - "open domain question answering using early fusion of knowledge bases and text"

Connected through hyperedge HE-2 (induced by source paper "evaluating logical generalization in graph neural networks"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "enhancing the transformer with explicit relational encoding for math problem solving"
    - "are powerful graph neural nets necessary a dissection on graph classification"
    - "clutrr a diagnostic benchmark for inductive reasoning from text"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "graph neural tangent kernel fusing graph neural networks with graph kernels"
    - "pytorch an imperative style high performance deep learning library"
    - "on training recurrent neural networks for lifelong learning"

Connected through hyperedge HE-3 (induced by source paper "hgat hierarchical graph attention network for fake news detection"):
  with members:
    - "fake news a survey of research detection methods and opportunities"
    - "fakedetector effective fake news detection with deep diffusive neural network"
    - "how powerful are graph neural networks"
    - "cimtdetect a community infused matrix tensor coupled factorization based method for fake news detection"
    - "detection and analysis of 2016 us presidential election related rumors on twitter"
    - "automatic detection of fake news"
    - "a stylometric inquiry into hyperpartisan and fake news"
    - "beyond news contents the role of social context for fake news detection"

Connected through hyperedge HE-4 (induced by source paper "query2box reasoning over knowledge graphs in vector space using box embeddings"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "on understanding knowledge graph representation"
    - "deep sets"
    - "hierarchical density order embeddings"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "improved representation learning for predicting commonsense ontologies"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "traversing knowledge graphs in vector space"

Connected through hyperedge HE-5 (induced by source paper "message passing for query answering over knowledge graphs"):
  with members:
    - "contextual graph attention for answering logical queries over incomplete knowledge graphs"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "deep sets"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "compositional vector space models for knowledge base completion"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-6 (induced by source paper "knowledge based conversational search"):
  with members:
    - "message passing for complex question answering over knowledge graphs"
    - "query expansion techniques for information retrieval a survey"
    - "open data chatbot"
    - "towards a question answering system over the semantic web"
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "interpretation of natural language rules in conversational machine reading"
    - "explainable reasoning over knowledge graphs for recommendation"

Connected through hyperedge HE-7 (induced by source paper "clutrr a diagnostic benchmark for inductive reasoning from text"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "on training recurrent neural networks for lifelong learning"
    - "systematic generalization what is required and can it be learned"
    - "relational recurrent neural networks"
    - "deep contextualized word representations"
    - "compositional attention networks for machine reasoning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"

Connected through hyperedge HE-8 (induced by source paper "deep graph library towards efficient and scalable deep learning on graphs"):
  with members:
    - "simplifying graph convolutional networks"
    - "star transformer"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "towards efficient large scale graph neural network computing"
    - "hierarchical graph representation learning with differentiable pooling"
    - "modeling polypharmacy side effects with graph convolutional networks"

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.SI(Social and Information Networks)