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: "audio visual scene aware dialog avsd challenge at dstc7"
  Abstract: Scene-aware dialog systems will be able to have conversations with users about the objects and events around them. Progress on such systems can be made by integrating state-of-the-art technologies from multiple research areas including end-to-end dialog systems visual dialog, and video description. We introduce the Audio Visual Scene Aware Dialog (AVSD) challenge and dataset. In this challenge, which is one track of the 7th Dialog System Technology Challenges (DSTC7) workshop1, the task is to build a system that generates responses in a dialog about an input video

Connected through hyperedge HE-1 (induced by source paper "factor graph attention"):
  with members:
    - "a simple baseline for audio visual scene aware dialog"
    - "diverse and coherent paragraph generation from images"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "structured attention networks"

Connected through hyperedge HE-2 (induced by source paper "dialog system technology challenge 7"):
  with members:
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "analyzing assumptions in conversation disentanglement research through the lens of a new dataset and model"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "end to end conversation modeling track in dstc6"
    - "attention strategies for multi source sequence to sequence learning"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "enhanced lstm for natural language inference"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "reactive multi stage feature fusion for multimodal dialogue modeling"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "fusionnet fusing via fully aware attention with application to machine comprehension"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "hadamard product for low rank bilinear pooling"
    - "dynamic memory networks for visual and textual question answering"
    - "effective approaches to attention based neural machine translation"
    - "vqa visual question answering"

Connected through hyperedge HE-4 (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"
    - "context attention and audio feature explorations for audio visual scene aware dialog"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "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-5 (induced by source paper "exploring context attention and audio features for audio visual scene aware dialog"):
  with members:
    - "context attention and audio feature explorations for audio visual scene aware dialog"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "from film to video multi turn question answering with multi modal context"):
  with members:
    - "on the convergence of adam and beyond"
    - "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"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "image grounded conversations multimodal context for natural question and response generation"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "the kinetics human action video dataset"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-7 (induced by source paper "end to end audio visual scene aware dialog using multimodal attention based video features"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "the kinetics human action video dataset"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "cnn architectures for large scale audio classification"
    - "soundnet learning sound representations from unlabeled video"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"

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)