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: "video fill in the blank using lr rl lstms with spatial temporal attentions"
  Abstract: Given a video and a description sentence with one missing word (we call it the "source sentence"), Video-Fill-In-the-Blank (VFIB) problem is to find the missing word automatically. The contextual information of the sentence, as well as visual cues from the video, are important to infer the missing word accurately. Since the source sentence is broken into two fragments: the sentence's left fragment (before the blank) and the sentence's right fragment (after the blank), traditional Recurrent Neural Networks cannot encode this structure accurately because of many possible variations of the missing word in terms of the location and type of the word in the source sentence. For example, a missing word can be the first word or be in the middle of the sentence and it can be a verb or an adjective. In this paper, we propose a framework to tackle the textual encoding: Two separate LSTMs (the LR and RL LSTMs) are employed to encode the left and right sentence fragments and a novel structure is introduced to combine each fragment with an "external memory" corresponding the opposite fragments. For the visual encoding, end-to-end spatial and temporal attention models are employed to select discriminative visual representations to find the missing word. In the experiments, we demonstrate the superior performance of the proposed method on challenging VFIB problem. Furthermore, we introduce an extended and more generalized version of VFIB, which is not limited to a single blank. Our experiments indicate the generalization capability of our method in dealing with such more realistic scenarios.

Connected through hyperedge HE-1 (induced by source paper "accept synthetic objects as real end to end training of attentive deep visuomotor policies for manipulation in clutter"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "pyrobot an open source robotics framework for research and benchmarking"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "craves controlling robotic arm with a vision based economic system"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "deep object centric representations for generalizable robot learning"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"

Connected through hyperedge HE-2 (induced by source paper "visual text correction"):
  with members:
    - "convolutional sequence to sequence learning"
    - "a dataset and exploration of models for understanding video data through fill in the blank question answering"
    - "language modeling with gated convolutional networks"
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "dynamic memory networks for visual and textual question answering"
    - "exploring models and data for image question answering"
    - "vqa visual question answering"
    - "a dataset for movie description"

Connected through hyperedge HE-3 (induced by source paper "soccernet a scalable dataset for action spotting in soccer videos"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "what actions are needed for understanding human actions in videos"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "temporal action detection with structured segment networks"
    - "learnable pooling with context gating for video classification"
    - "temporal context network for activity localization in videos"
    - "action tubelet detector for spatio temporal action localization"
    - "actionvlad learning spatio temporal aggregation for action classification"

Connected through hyperedge HE-4 (induced by source paper "a joint sequence fusion model for video question answering and retrieval"):
  with members:
    - "learning from video and text via large scale discriminative clustering"
    - "tgif qa toward spatio temporal reasoning in visual question answering"
    - "hadamard product for low rank bilinear pooling"
    - "a dataset and exploration of models for understanding video data through fill in the blank question answering"
    - "learning language visual embedding for movie understanding with natural language"
    - "cnn architectures for large scale audio classification"
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "video fill in the blank with merging lstms"

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.CV(Computer Vision and Pattern Recognition)