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: "context attention and audio feature explorations for audio visual scene aware dialog"
  Abstract: With the recent advancements in AI, Intelligent Virtual Assistants (IVA) have become a ubiquitous part of every home. Going forward, we are witnessing a confluence of vision, speech and dialog system technologies that are enabling the IVAs to learn audio-visual groundings of utterances and have conversations with users about the objects, activities and events surrounding them. As a part of the 7th Dialog System Technology Challenges (DSTC7), for Audio Visual Scene-Aware Dialog (AVSD) track, We explore `topics' of the dialog as an important contextual feature into the architecture along with explorations around multimodal Attention. We also incorporate an end-to-end audio classification ConvNet, AclNet, into our models. We present detailed analysis of the experiments and show that some of our model variations outperform the baseline system presented for this task.

Connected through hyperedge HE-1 (induced by source paper "learning question guided video representation for multi turn video question answering"):
  with members:
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "entropy enhanced multimodal attention model for scene aware dialogue generation"
    - "reactive multi stage feature fusion for multimodal dialogue modeling"
    - "audio visual scene aware dialog"
    - "motion appearance co memory networks for video question answering"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "tvqa localized compositional video question answering"

Connected through hyperedge HE-2 (induced by source paper "leveraging topics and audio features with multimodal attention for audio visual scene aware dialog"):
  with members:
    - "audio visual scene aware dialog"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "audio visual scene aware dialog avsd challenge at dstc7"
    - "aclnet efficient end to end audio classification cnn"
    - "attention is all you need"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "cnn architectures for large scale audio classification"
    - "building end to end dialogue systems using generative hierarchical neural network models"

Connected through hyperedge HE-3 (induced by source paper "exploring context attention and audio features for audio visual scene aware dialog"):
  with members:
    - "audio visual scene aware dialog avsd challenge at dstc7"
    - "aclnet efficient end to end audio classification cnn"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "cnn architectures for large scale audio classification"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "vqa visual question answering"
    - "video paragraph captioning using hierarchical recurrent neural 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.CL(Computation and Language)