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: "compositional vector space models for knowledge base completion"
  Abstract: Knowledge base (KB) completion adds new facts to a KB by making inferences from existing facts, for example by inferring with high likelihood nationality(X,Y) from bornIn(X,Y). Most previous methods infer simple one-hop relational synonyms like this, or use as evidence a multi-hop relational path treated as an atomic feature, like bornIn(X,Z) -> containedIn(Z,Y). This paper presents an approach that reasons about conjunctions of multi-hop relations non-atomically, composing the implications of a path using a recursive neural network (RNN) that takes as inputs vector embeddings of the binary relation in the path. Not only does this allow us to generalize to paths unseen at training time, but also, with a single high-capacity RNN, to predict new relation types not seen when the compositional model was trained (zero-shot learning). We assemble a new dataset of over 52M relational triples, and show that our method improves over a traditional classifier by 11%, and a method leveraging pre-trained embeddings by 7%.

Connected through hyperedge HE-1 (induced by source paper "vqa lol visual question answering under the lens of logic"):
  with members:
    - "towards vqa models that can read"
    - "lxmert learning cross modality encoder representations from transformers"
    - "why does a visual question have different answers"
    - "ok vqa a visual question answering benchmark requiring external knowledge"
    - "integrating knowledge and reasoning in image understanding"
    - "deep modular co attention networks for visual question answering"
    - "from recognition to cognition visual commonsense reasoning"
    - "a corpus for reasoning about natural language grounded in photographs"

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"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (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-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 "lifelong and interactive learning of factual knowledge in dialogues"):
  with members:
    - "towards an engine for lifelong interactive knowledge learning in human machine conversations"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "listen interact and talk learning to speak via interaction"
    - "towards a continuous knowledge learning engine for chatbots"
    - "one shot relational learning for knowledge graphs"
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"
    - "naturalizing a programming language via interactive learning"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-6 (induced by source paper "iteratively learning embeddings and rules for knowledge graph reasoning"):
  with members:
    - "improving knowledge graph embedding using simple constraints"
    - "from knowledge graph embedding to ontology embedding an analysis of the compatibility between vector space representations and rules"
    - "knowledge graph embedding with iterative guidance from soft rules"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "kbqa learning question answering over qa corpora and knowledge bases"
    - "analogical inference for multi relational embeddings"

Connected through hyperedge HE-7 (induced by source paper "transgcn coupling transformation assumptions with graph convolutional networks for link prediction"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "a comprehensive survey on graph neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "adaptive graph convolutional neural networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "embedding logical queries on knowledge graphs"
    - "large scale learnable graph convolutional networks"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"

Connected through hyperedge HE-8 (induced by source paper "relation embedding with dihedral group in knowledge graph"):
  with members:
    - "understanding straight through estimator in training activation quantized neural nets"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "toruse knowledge graph embedding on a lie group"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "knowledge base completion baselines strike back"

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)