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: "chains of reasoning over entities relations and text using recurrent neural networks"
  Abstract: Our goal is to combine the rich multistep inference of symbolic logical reasoning with the generalization capabilities of neural networks. We are particularly interested in complex reasoning about entities and relations in text and large-scale knowledge bases (KBs). Neelakantan et al. (2015) use RNNs to compose the distributed semantics of multi-hop paths in KBs; however for multiple reasons, the approach lacks accuracy and practicality. This paper proposes three significant modeling advances: (1) we learn to jointly reason about relations, entities, and entity-types; (2) we use neural attention modeling to incorporate multiple paths; (3) we learn to share strength in a single RNN that represents logical composition across all relations. On a largescale Freebase+ClueWeb prediction task, we achieve 25% error reduction, and a 53% error reduction on sparse relations due to shared strength. On chains of reasoning in WordNet we reduce error in mean quantile by 84% versus previous state-of-the-art. The code and data are available at this https URL

Connected through hyperedge HE-1 (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"
    - "embedding logical queries on knowledge graphs"
    - "hierarchical density order embeddings"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "improved representation learning for predicting commonsense ontologies"
    - "traversing knowledge graphs in vector space"

Connected through hyperedge HE-2 (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"
    - "embedding logical queries on knowledge graphs"
    - "compositional vector space models for knowledge base completion"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "reasoning driven question answering for natural language understanding"):
  with members:
    - "on the capabilities and limitations of reasoning for natural language understanding"
    - "evidence sentence extraction for machine reading comprehension"
    - "question answering as global reasoning over semantic abstractions"
    - "going on a vacation takes longer than going for a walk a study of temporal commonsense understanding"
    - "cogcomptime a tool for understanding time in natural language text"
    - "annotation artifacts in natural language inference data"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "multi hop knowledge graph reasoning with reward shaping"

Connected through hyperedge HE-4 (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-5 (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-6 (induced by source paper "differentiable reasoning on large knowledge bases and natural language"):
  with members:
    - "nlprolog reasoning with weak unification for question answering in natural language"
    - "towards neural theorem proving at scale"
    - "a survey on open information extraction"
    - "learning explanatory rules from noisy data"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "billion scale similarity search with gpus"
    - "end to end differentiable proving"

Connected through hyperedge HE-7 (induced by source paper "dykgchat benchmarking dialogue generation grounding on dynamic knowledge graphs"):
  with members:
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "variational knowledge graph reasoning"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "key value retrieval networks for task oriented dialogue"
    - "a knowledge grounded neural conversation model"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"

Connected through hyperedge HE-8 (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"

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.CL(Computation and Language)